1 1 Abstract—With the advent of the technologic

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1
Abstract—With the advent of the technological world, the
technology is getting more and more advanced day-by-
day. Artificial Intelligence (AI) can possibly affect pretty
much every part of medical care, from identification to
forecast and anticipation. The appropriation of new
advances in medical services, nonetheless, slacks far
behind the rise of new advances. An elementary
understanding of developing Artificial Intelligence
proceedings can be basic though wellbeing couldn't care
less experts. These advancements incorporate master
frameworks, mechanical cycle robotization, regular
language preparing, Artificial Intelligence, and deepest
understanding. In the research article, different

technologies have been derived for the detection of
different health diseases. First of all, background
knowledge has been taken under consideration. After
that, diseases like Diabetes, Alzheimer’s disease and
health disease have been discussed. It has been evaluated
that technologies are providing extremely efficient results
with higher level of accuracy which shows that the
discussed technologies are contributing at their best level.
The proposed methods for the discussed diseases in
different research articles have also been evaluated and
highlighted. Every technology has its own benefits. The
proposed article illustrate that how Artificial Intelligence
is contributing in healthcare department and in the
detection of different health diseases.
Index Terms— Expert System, Decision making
Support, Artificial Intelligence, Clinical Decision Support
System, Magnetic Resonance Imaging (MRI), Alzheimer’s

Disease
I. INTRODUCTION
A. Artificial Intelligence
Artificial intelligence is how different machines exhibit
intelligence compared to natural intelligence used by different
humans and animals. In simple words, the theory related to
the growth of computer systems to perform tasks usually
needs human intelligence, for instance, visual perceptions,
decision making, translation of languages, and speed
recognition (Fei Jang, 2017). It is known as a digital
computer's capability or called a computer-controlled robot to
execute tasks usually connected with intelligence. This term
AI is applied to those projects related to developing systems
bestowed with factors of human or intellectual processes, for
example, the ability to reason, generalizing, abstracting, learn
from past experiences, or to discover meaning. In the 1940s,
digital computers evolved and came into existence, so from
1940, since now, computers are designed to perform

complicated and complex tasks, for instance, working on
advanced proofs and theorems from mathematical portions as
well as playing chess. Despite continued advances in the
speed of computer processing and memory capacity still, there
is a gap in programming that they cannot be as flexible as
human beings. This system is not proficient in broader
Expert System (AI) for Decision making Support
Abdullah Alshathri 442105936
College of Computer and Information Sciences
King Saud University, Riyadh, Saudi Arabia
[email protected]
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domains or tasks requiring a vast level of information and
knowledge (Kun-Hsing Yu, 2018).
On the contrary, some programs have gathered the program's
level of exports to the execution of specific tasks; hence,

artificial intelligence is found in applications playing multiple
medical diagnosis roles, search engines, or handwritten plus
voice identification.
However, it has been studied that all human behavior is
known as intelligence, but psychologists do not consider
human intelligence by only one trait but with multiple
abilities. Artificial Intelligence mainly focuses on learning,
reasoning, solving problems, anticipations, and utilizing
different languages.
In the learning process, the simple form is trial and error. It
merely means that memorizing several items and process are
called rote learning in artificial intelligence. This term is
comparatively easy to learn and understand. Moving further,
developing and executing more challenging tasks are known
as generalizations. It involves using previous past experiences
to new and analogous situations (Yaping Zang, 2015). It can
be explained through the example that the program used to
work and understand past tense would be difficult for that

program to understand English in present tenses. The
reasoning is also an essential characteristic of artifi cial
intelligence, which works based on inferences. Inferences are
always of two types, Deductive and Inductive. In the
deductive method, the reasoning is made based on the
hypothetical method and the base of illustrations and
examples.
On the other hand, in the inductive method, the reasoning is
based on facts and figures. Problem-solving is a factor that
works on the systemic approach through a range of specific
actions. Problem-solving is always done to achieve
predefined targets and objectives (Thomas M, 2019). In
perception, the environment is examined through different
means of certain sensory parts, real or artificial. The last
factor of artificial intelligence is language. It is a system that
depicts meaning by old conventions. It is also said that
language is required to be confined in terms of speaking
words. The traffic signal is a clear example of language under

artificial intelligence. A productive language can execute a
diverse variety of sentences and words.
However, artificial intelligence is contributing to the health
sector of different countries positively. Many countries are
availing benefits by using artificial intelligence to diagnose
diseases and even then, in the treatment of those diseases.
This study is conducted to find the impact of artificial
intelligence in the health sector (Sandeep Reddy, 2019).
B. Artificial Intelligence in Healthcare
The paper is about the artificial intelligence in health care
departments and with the diagnosing diabetes and Alzheimer.
AI in health department has enormously populated in last
decades and is peak potential to deliver the control and
establish proper systems to diagnose and propose treatments
for health care and severe diseases like diabetes and
Alzheimer (Trishan Panch, 2018). With Artificial Intelligence
and its contribution in health care field plots so much
information but yet there is a need to explore more of artificial

intelligence helping in health care as well as there is a need to
grow in artificial intelligence field. To study the health
professions, evolve in this field and the contribution of
artificial intelligence will show remarkable changes in field.
Artificial intelligence creates and stimulate the human-like
intelligent behavior in complex machines (Brian Wahl, 2018).
It is used in health cares and most importantly it used mostly
in human health care, like diagnosing diseases, in process of
treatment, prognosis and predictive health service.
Artificial Intelligence needed more exploration and work to
make use of its various fields related to human, like chemical
engineering, management and human health aspects in severe
diseases. It’s in early development and need more focus with
respect to clinical readiness. An artificial intelligence
followed in major experimental and conceptual health care
evolutions and domains. In future, almost every kind of
practitioners from specific analyst to coroner by applying
Artificial intelligence theories and constitute a great part in

teach (Shizhen Wang, 2019). The study aims to give brief
description about the artificial intelligence usage in health
care with severe diseases like diabetes, Alzheimer and heart
problems. The description is important as it improves the part
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of literature and benefit in using most part in literature in
future. In previous research there is a vast information present
about radiology.
Which is used previously but today the concept of radi ology
is transformed in to artificial intelligence and automation.
According to research the professions will be impacted
differently for different fields or disciplines like discipline
bounded quarterly by reliance on interconnection, task should
be repetitive and data reliance with creativity level should be
appropriate (Arash Shaban-Nejad, 2018). The dermatology

and radiology varying degrees more influence on profession
but varying less in case of profession as dentistry thus the use
of artificial intelligence being predicted to be useful for the
fields in which more complexity is being seen. Online
predicted the upcoming health hazards and pro-actively
perform functions to overcome disease.
The predictive record gets through social media or online
media electronic health care records. The control of disease is
possible by geocoding the information regarding the disease
spread and help epidemiologists to accurately monitor and
control the severe disease before it spread out rapidly.
Artificial intelligence used in various fields like robotics,
image and voice recognition and natural language to
processing expert system. With its growing capabilities and
dynamic approaches and broad rapidly growing techniques
with upgrading system it is widely used in medicine field
early in 1950 when physicians successfully diagnose the
improvement in treatment due to computer aided program. Al

categorize in to two types, the capability of representing
human mind and perform all intellectual task to perform
human task.
II. LITERATURE REVIEW
Expert system is known as knowledge-based system. The ES
system is said to be an expert system which is very competent
to resolve specific problems like an expert level. The creation
of expert system is also named as knowledge engineering.
Expert system comprises of two main prime components
(David Wiljer, 2019). Just like Knowledge base and reasoning
engine. The knowledge base is known as the expert system
which typically involve the knowledge-based system, include
the basic knowledge of specific need or problem. And the
reasoning engine is known as constituting the complex rules
just like if-then statements and include information which is
incomplete and uncertain. The fuzzy logic is being developed
full of uncertainty and probability which is shown under
mathematical principles. This indicates the progression of

expert system in just few years. The developing nature of
fuzzy logic make it more specific and help to deduce the
uncertainty and the way human can approach to solve difficult
constituting excessive uncertainty. In South Africa logic used
in diagnosing chronic condition is fuzzy logic and diagnose
steps to minimize the chronic disease, cholera outbreaks
condition to normalize with fuzzy logics.
Application of Artificial Intelligence: Machine Learning
Artificial Intelligence include one of the applications named
Machine Learning. The data automation and data analysis
include which perform by using algorithm which conclude
different patterns in return master from them. Machine
learning categorize in to three forms like supervised learning,
with unsupervised learning and reinforcement learning. So,
the first type of machine learning is to identify the training
data. With this in contrast the applications of learning carry
ways to find the patterns that comprises data. The next level
of supervised learning is reinforcement learning in which the

reward and basic punishment are given when application
interacts with complex environment. When you talk about
machine learning that is unsupervised machine learning
relating with data mining and includes to exclude the identify
patterns in huge datasets. Like the disease used to learn and
creating logic to create machine learning in order to diagnose
the treatment and constitute various treatment but anyhow
diabetes is long lasting disease, and create full long-lasting
effects on lungs.
A. Diabetes
It considers to be long-lasting disease and effects on limbs and
vital organs in body. The ES used to identify the issue and
create such techniques or methods or treatments to control the
disease, detect it and manage to create tools and this all be
done with the help of physicians. According to Diabetes
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Research Center, the early diagnosis of patients can prevent
them from 80% complications of type II diabetes. (Nesreen
Samer El_Jerjawi, 2018) The two types of diabetes type I and
type II. The type I diabetes refers to insulin dependent and
second type is insulin deficiency. (Mercedes Rigla, 2017) The
complications in diabetes further categorize in to two main
types, which are vascular and nonvascular complications of
diabetes. Vascular include micro vascular diseases such as eye
disease, neuropathy and macro vascular refers to coronary
disease and peripheral vascular disease. Whereas non vascular
complications lead to gastro paresis, sexual dysfunction and
skin disease.
B. Alzheimer
The most curious and integral part of human body is brain.
The most important elements of the human brain incorporate
reasoning, thinking, responsibility for coordination and parity
meaning voluntary movements, coordination and balance,
executive planning, language, memory and learning,

mathematical logic, and emotional responses. As the brain is
the center of the nervous system any abnormal conduct or
working of it might cause all out breakdown of the whole-
body functionalities. Such an abnormal conduct may cause
Alzheimer's disease. Some 4.5 million Americans have
Alzheimer's disease (AD), the eighth top reason for death in
2001. Principally older people are victims of AD, about 12.8
% of those aged more than 65 years. 35 to 40 % of those aged
more than 80 years are affected (K.S.Biju, 2017).
It causes issues with thinking, memory and behavior. It occurs
mostly in older adults about 65. Presently there is no antidote
or treatment available for this disease as of today. But
Researchers and scientists are making an effort to find
treatment for this disease which can help ease pain of the
patients.
The medical imaging technique Magnetic Resonance imaging
used basically in radiology to form pictures of the
physiological processes of the body and anatomy. MRI

scanners use strong magnetic fields, magnetic field gradients,
and radio waves to generate images of the organs in the body.
Segmentation of the MRI brain image is commonly used
technique for measuring and visualizing the brain's cellular
structures in a meaningful and easiest way to analyze the
various types of diseases such as Alzheimer's disease,
dementia and Brain tumor. Segmentation techniques are
various which are available today and, in this study, it is
described in this literature part. In general, the following
categories in which segmentation is divided: Histogram based
technique (Chinnu, 2013), Threshold based technique (Priya,
Segmentation of Brain Tissue in MR Brain Image using
Wavelet Based Image Fusion with Clustering, 2013), hybrid
technique (Palanisamy, 2010), Edge based technique (Patil,
2012), Region based technique (Jaafar, 2011), Cluster based
technique (P.Kalavathi, 2015) and Classification based
technique. In order to detect the AD, a method presented in
(Kalavathi, 2017) segmentation of WM and GM from non-

AD and ADl MRI brain scans Contour based brain
segmentation method (CBSM), Fast Fuzzy C Means (FFCM)
is used. For giving a real set (Toro, 2018) of neighborhoods
for the histone-calculation method which helps identify
Alzheimer’s disease, an overly segmented process performed
using average volume for WM, GM and cerebrospinal fluid.
Another paper using the WM and GM techniq ue extracted (Y-
D. Zhang, 2018) gray matter images, Afterwards, PCA were
put in for feature extraction (Kalavathi Palanisamy, 2017).
Total principal components (PC) that were extracted from
data of 3D MRI scan using singular value decomposition
(SVD) algorithm were 20. A kernel support vector machine
decision tree was built which helped in binary classification
of Alzheimer’s disease and non- Alzheimer’s disease MRI
images. For segmentation of MRI brain images an advanced
U-NET (Hanane Allioui, 2019) architecture is proposed
which can identify Alzheimer disease and brain tissue damage
and an accurate and exact Alzheimer’s disease detection using

an advanced full neural network in a 2.5D context.
Segmentation side by side with clustering also proved useful.
Fuzzy c-means clustering is an unsupervised clustering
method that segments MRI brain images into clusters
(different regions depending upon type of pixels in MRI) with
similar spectral properties. The brain tissues segmentation
using PSO based clustering techniques was developed to
detect Alzheimer’s disease in MR brain images (Yu-Dong
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Zhang, 2014). The main idea behind this method is the mixed
use of Gaussian Mixture Model and K-Means Algorithm
(GKA). The MRI brain images will be segmented into WM,
GM and CSF by acquiring GKA method. Morphological
operations are also used in segmentation (Jyothi, 2015) for
that to work properly MRI brain images are taken as input and

morphological operations are applied to detect abnormalities
of brain tissues and cells for the diagnosis of Alzheimer’s
disease. Often one or the other methods from morphological
operations are combined with FMRI or Tomography to
provide complementary information about normal and
abnormal brain function. Edge detection is also used for
object detection.
Segmentation techniques are readily used in detection of
Alzheimer’s disease. The image segmentation plays a vital
role in diagnosing processes and analyzing diseases like
Alzheimer’s disease. A method to detect the brain tissue is
segmentation which is Alzheimer’s disease affected MRI
head scans was proposed. For detection of brain tissues and
cells in the brain MRI images SFCM was proposed in
combination with spatial fuzzy clustering algorithm. Another
Algorithm along with segmentation technique (R. Anitha,
2016) is watershed including highly reserved up to its limits
work to explore disease in an image which is scanned.

Segmentation of brain tissues are very helpful according to
(Kazuhito Sato, 2011) A brain tissue segmentation is done
using two kinds of unsupervised neural networks which are
Fuzzy Adaptive Resonance Theory (ART) and Self-
Organizing Maps (SOMs) and for classification of
Alzheimer’s disease.
C. Health Informatics and Electronic Medical Records
The health information regarding the health care issues like,
severe diseases or chronic disease will provide information to
various treatments and methods to use in treatment of disease.
The electronic media records health information that
describes the acquisition, retrieval, stored information and use
of health care information to positively affect the patient
overall health across interaction with health system. The
EMRs use to identify the resources used to provide patient as
much needed treatment in a specific time. It ensures to provide
the critical information for making sound policies and
program decisions (Karthik Seetharam MD, 2019). The

EMRs beneficial to attain information regarding population
and health information and it is the important source to get the
health information.in this era it is in great use in low resource
settings which explained the potential application of AI
platform improve public health informatics and decision
making. An important growth in AI explained with an
example of establishing Open MRS system which is used
among 15 African countries to make use of EMR system to
and implementation of MER system is known as BORA. This
focus on improving BORA to improve women maternal and
child health problems. In South Africa logic used in
diagnosing chronic condition is fuzzy logic treatment in
mostly rural areas. According to research the use of AI
become useful from many years as it helped to increase the
collection of data and closely relate the critical gap.
D. Cloud Computing
Cloud computing enhance the exploration of AI application
for health care centers. Thus, it refers to multiple functioning

in a system like to store, control and have access and doing
many processes like remote servers rather than single personal
computer or hard drive. Many businesses start to explore
cloud computing because of its phenomenal advantage in IT
system, enhance the validity and reliability with enhancing
ways to save cost (Mohamed Elhoseny, 2018). Previously it
in not accessible and unattainable in low- and middle-income
countries but now it easily accessible and provide companies
provide cloud computing in organization built in rural or
urban areas. The EMRs can be controlled with proper
functioning of privacy with security systems. The cloud
computing makes it enable to use the diverse data related to
public health. The cloud computing main advantage is to
enable the implementation of application that interlinked with
IT building in settings where its existence is rare. The cloud
computing application used in deriving the disease in patient
and aimed to improve the interactive voice response of
telephone calls for managing non-communicable disease in

Honduras. There is so less upgrading of IT building
institutions in our country but the new inventions are
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effective. The digital health technologies reached to the
advancement level with not very high quality of resources in
high earning population of country are benefiting from
integrating AI into their healthcare ecosystem. According to
research that the AI applications results in estimated cost of
more than $145 billion in healthcare by yearly 2026
approximately in USA. Many other prove transformation with
help of AI approve the benefit of using Artificial intelligence
for countries constituting poor health resources (Giuseppe
Aceto, 2020). Today the data brings out to be vast that this
created a plethora to improve individual and group level
health care by using AI. The development in cloud computing
and substantial investments in health care information and

introduction in mobile health application will increase the
exploration of AI in health care field and approve to be
successfully in the medicine field to results in betterment of
health care problems in world.
E. Mobile Health
The mobile health use cellphone, and radial automation to
acquire fitness care information and the accessibility of cell
phones rapidly increase in less-paying territories has designed
many ways to use automations to reinforce fitness issues. Cell
phone mostly utilized in Community Health Workers for
betterment of the provision of fitness related services with
poverty-stricken setting (Giovanna Sannino, 2019). The tool
of flexible health is currently evaluated and cell phones
utilized to coordinate the fitness issues to patient in lower
income areas at present where recourses are less and physical
cooperation are not practical. Usage of phone calls plus
messages assistance to communicate request hurdles to
inoculation and enhance potentially thoroughly written

through random controlled trials in setting in Kenya and USA.
III. BACKGROUND
A. Advent of Artificial Intelligence (AI)
In today's world, with a lot of advancement in technology and
digitization, AI is also contributing to its best to support the
decision-making process. There are infinite examples of AI
applications that are playing their efficient role in
technological advancements. Does AI have some common
constitutes that elaborate the definition of what artificial
intelligence is? Four concepts explain it well, something that
behave intellectually, according to, 1) situation for
environment and its demand, 2) is it resilient towards the
alternating goals, 3) intellectual towards experience, and 4)
make appropriate decisions towards intuitive and computing
environment.
B. Related Work on Healthcare
Artificial intelligence came into existence in the mid of 1940.
In today's era, it is known as a subfield of computer science-

related to symbolic analysis methods by computer. Alan
Turing designed AI to explain the intelligence of computers
and technology. Artificial Intelligence possesses a
tremendous and positive impact on the health sector. The
primary purpose of artificial intelligence is to use computers
in an efficient way to solve challenges and hurdles in the
healthcare sector (Ezekiel J. Emanuel, 2019). By using
computers, data can be interpreted, which can be received by
identifying chronic diseases, for instance, diabetes,
Alzheimer's well several types of cancers. In the complicated
world of health care management, artificial intelligence is
used to support human medical staff to provide better and
faster services, diagnose problems, and interoperate data to
discover data trends that would help treat specific di seases.
Artificial intelligence has been used in health care to
understand deep learning in the identification of diseases. This
is the area where artificial intelligence has shown promising
success in diagnostics. In the learning of machines and the

radiology sector, artificial intelligence has been used at a vast
level. It has been seen that in performing automotive
administrative related tasks, health AI has performed very
well. It also helped in the reduction of operational costs. By
using artificial intelligence, some hardcore challenges in
health care have been resolved. It is also seen that many
patients, through this process, find health back due to the
diagnoses of chronic diseases (Thomas Davenport, 2019).
When artificial intelligence was implemented in healthcare,
the first time was in late 1970 when Dendral was discovered
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at Stanford University USA. It was used to guide chemists to
find some not known organic molecules. After that successful
identification, it was used in the health care sector of different
economies. Today, artificial intelligence has brought so much

worth and value in health care management by developing so
fastly and rapidly. It is also anticipated that AI will continue
to achieve success in the health care department in the near
future. It has prominently contributed to the field of diagnosis
(Mccall, 2020). To identify any disease, it needs tons of data
to analyze medical imaging, medical records of patients, and
the history of patients. It is also essential to understand that
the genetics of patients require in the diagnosis of disease. In
the last decade, it has been observed that artificial intelligence
has been more accurate in finding disease and even in
recommending effective treatment—for example, cancer
diagnoses (Marzyeh Ghassemi, 2019).
Simple radiology is not enough for the procurement of illness
i.e. cancer. Artificial intelligence is beneficial in this manner.
AI algorithm has been introduced by South Korea, through
which cancer cells can be identified on X rays images.
Moreover, mammography pictures are used to detect lungs
plus breast cancer. Artificial intelligence has 97 % accuracy

in identifying these lungs and breast cancers in the human
body. Moreover, artificial intelligence has helped in saving
around 3$ billion every year in the health care department.
Moving further, the contribution of artificial intelligence also
has significance in robot surgery. This is the most critical
application in artificial intelligence, specifically in health care
management. This application provides benefits in two ways,
a tremendous amount of money-saving and more successful
and effective surgery. In this surgery, Accenture estimated
that this robot surgery could save around 40$ billion in
healthcare industry per year. Furthermore, artificial
intelligence also helps in managing a large amount of data in
the healthcare industry. While dealing with a large amount of
data, it near impossible to have proficiency in all gigabytes. A
considerable amount of data comprises of billion and trillion
of entries about patients, surgical process and procedures,
drugs, treatments, data related to different researches, and
many more. If this data is required to be used regularly, it

should be stored and managed efficiently. Artificial
intelligence work accordingly to manage this sort of data. It
provides much better and improved medical care to patients
that collecting data and storing it makes it significantly more
comfortable. Big data also helps collect information about
demographics and medical data, for example, lab tests,
medical history and condition, clinical data. However, it also
helps to provide efficient operational information, and more
importantly, it supports the research and development
proficiency. With the guidance of predictive analysis of
efficiency of staff members companies in healthcare can
boycott expensive operational costs. But by using artificial
analysis, big data can help manage workflow and give
effective forms of costs. In terms of research and
development, it can help to work on several tasks of drugs and
clinical trials. It also can find some patients with biological
factors who will indulge in specialized clinical practices
(Rigby, 2019).

It has also been observed from the past that artificial
intelligence has helped find a cure for diseases. More
availability of data can help in providing a cure at the early
stages. Hidden patterns, not known correlated diseases, and
insights can be identified early using artificial intelligence in
health care management. This credit goes to AI as big data can
help anticipate the chances and probabilities of certain
diseases in a specific human body are more or less. By
collecting data on family history and demographics can help
in detecting diseases appropriately and positively. From the
above discussion in background, indeed, it is concluded that
artificial intelligence has numerous healthcare industry
benefits. With time, AI would help achieve success and
growth in the healthcare management at a vast scale.
IV. DISCUSSION
The Artificial Intelligence (AI) has developed extensively
over the most recent 60 years. While there are currently
numerous Artificial Intelligence applications that have been

sent in top level salary nation settings, use in asset helpless
settings remains generally early. With a couple of prominent
special cases, there are restricted models of Artificial
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Intelligence being utilized in such settings. Notwithstanding,
there are signs that this is evolving (Jarrahi, 2018). A few
prominent gatherings have been gathered lately to talk about
the turn of events furthermore, organization of Artificial
Intelligence applications to decrease neediness and convey an
expansive scope of basic public administrations. We give an
overall review of Artificial Intelligence and how it tends to be
utilized to improve wellbeing results in asset helpless settings
(Wahl, 2018). We additionally depict a portion of the current
moral discussions around quiet security also, safety. In spite
of current difficulties, Artificial Intelligence holds enormous
guarantee for changing the arrangement of medical care

administrations in asset helpless settings (Duan, 2019).
Numerous wellbeing framework leaps in such settings could
be overwhelmed with the utilization of Artificial Intelligence
and other correlative rising innovations. Further examination
furthermore, interests in the advancement of Artificial
Intelligence apparatuses custom fitted to asset helpless
settings will quicken acknowledging of the full capability of
Artificial Intelligence for improving worldwide wellbeing
(Behnam Malmir, 2017).
Many institutions and firms are developing and implementing
tools to accurately find out the problem and are proposing
decision support systems as a substitute or a solution towards
these types of problems (Shikhar Kr. Sarma, 2010). Likewise,
healthcare (Wiljer, 2019) departments are also using these
measures and methods to propose the solution for health
diseases. They are mainly focusing on the designing of tools
to detect and diagnose the diseases. This is because the
amount of information that they received using tools are

somewhat accurate but they are not perfect but contributing a
lot towards this diagnostic process. They are also trying to
design tools to take decisions about the diagnosed diseases.
These decision support systems (Balaji, 2010) will help them
a lot as the information gathered by the detection tools is so
vast and huge that human cannot understand and elaborate
easily. So in order to cater the issue, healthcare departments
are also focusing on the development of decision support
tools. Many tools have been designed and proposed to detect,
diagnose and the intervention treatment for the disease (Tan,
2016). In this research article we are going to discuss the way
researches and scientists are proposing and designing
techniques and technologies and how these developments are
contributing towards the betterment of human diseases
(Suryono, 2019).
Simulated intelligence holds huge guarantee for changing the
arrangement of medical care administrations in asset helpless
settings. A significant number of the wellbeing frameworks

obstacles in such conditions could be tended to and defeated
utilizing Artificial Intelligence upheld by other mechanical
turns of events and developing fields. The omnipresent
utilization of cell phones, joined with developing interests in
supporting innovations (e.g., m-Health, EMR and distributed
computing), give adequate occasions to utilize Artifici al
Intelligence applications to improve general wellbeing results
in low-pay nation settings. While we have given a few
instances of how Artificial Intelligence is now applicable to
the intend for the betterment of wellbeing results in less-pay
nations, many positively numerous Artificial Intelligence
applications previously being executed and unquestionably
there will be more in coming years.
Many vague solutions have been proposed from the past
years. Artificial intelligence (Elkin, 2018) is helping mainly
in this regard. Multiple solutions have been proposed for the
detection of diseases and the ways and methods used for their
detection using artificial intelligence techniques. It has been

already discussed in detail that how artificial intelligence is
playing its role in healthcare. Now we are going to discuss
specifically some diseases in which it is contributing.
Researchers are working on different diseases like diabetes
(Jacobs, 2020), Alzheimer (K.S.Bijua, 2017), heart, cancer,
skin diseases etc. We are going to discuss some of them i.e.
Diabetes, Alzheimer and tumor detection.
A. Use of Artificial Intelligence in Diabetes Detection
Many decision support systems are being proposed in
healthcare departments for different diseases. A vague theory
have been proposed in for the detection of Diabetes using
artificial intelligence techniques. It is broadly pointed that the
traditional ontologies can't adequately deal with uncertain
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what's more, unclear information for some true applications,

however vague cosmology can successfully resolve data and
information issues with vulnerability. The article (Chang-
Shing Lee, 2011) presents a novel vague master framework
for diabetes related procurement system. An architecture,
consisting fiver layers, vague metaphysics, counting a vague
information layer, vague gathering connection layer, vague
gathering space layers, vague individual connection layer, and
vague individual space layer, is created in the vague master
framework to depict information with vulnerability. The five
layer approach is well demonstrated by the below figure.
Fig. 1. Architecture of unique vague layer five system
By applying the novel vague philosophy to the diabetes space,
the semantic decision support agent is characterized to
demonstrate the diabetes information. The overall
architecture of vague diabetes philosophy is also designed in
the form of a diagram that demonstrates the concept.
Fig. 2. Architecture vague expert system for diabetes

application.
Moreover, a semantic decision support agent, counting an
information development component, vague philosophy
creating system, and semantic vague dynamic component, is
likewise evolved. The information development system
develops the vague ideas and relations dependent on the
structure of the vague philosophy creating system. The cases of
this system are created by the vague philosophy creating
component. At last, in view of above-mentioned system and the
vague philosophy, the semantic vague dynamic component
imitates the semantic illustration of clinical staff for diabetes
related application. Significantly, the suggested vague master
framework can turn out viably for diabetes choice help
application. In the end, the proposed methods is evaluated in
terms of precision, recall, accuracy, true negative rate and
specified threshold value.

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Fig. 3. Measuring performance of proposed method
B. Use of Artificial Intelligence in Alzheimer Detection
Disease of Alzheimer is one of the neurological confusions in
which nerve cells (neurons) connecting each other injures and
dies throughout the brain. The demise of cells of brain causes
loss of memory and diminish and leads to shrinkage in the brain
tissues. It’s a type of dementia. Indications for the most part
grow continuously and weaken after some time, getting
adequately outrageous to interfere with step by step exercises
and it leads towards death. The center foundations for
Alzheimer's infections are low mind movement and blood
stream. Magnetic Resonance Imaging (Priya, 2013) is
comprehensively utilized in distinguishing Alzheimer's
infection. In their examination work they intended to build up a
computational technique to measure the cerebrum tissue
misfortune in MRI checks utilizing Contour based mind division

strategy and Threshold based Segmentation. These techniques
separate White Matter from Gray matter which helps identify
Alzheimer's disease. To evaluate and estimate the accuracy of
the method proposed in their paper, the dataset used of MRI
brain images is obtained from kaggle website. In this dataset the
skull of the brain was already removed from a total of more
than
500 MRI brain images. Otherwise the skull can be removed with
Edge detection and morphological operation mentioned in
(Shanthi kj, 2013).
Disease of Alzheimer is mainly due to various kind of
dimension reduction in the cells of brain and cells normally
known as White Matter (WM) and Grey Matter (GM), therefore
segmentation of these brain tissue using MRI brain images leads
to identifying Alzheimer. Normally, partitioning the cells of
brain from Magnetic Resonance Imaging brain image is a tough
and tricky work, thus first the skull from the mind image must
be removed. Luckily the dataset they found and experimented
with was already processed and gone through this step. One of

automated skull removing methods is mentioned in the dataset
heading. In the proposed method, they used contour-based brain
segmentation method along with thresholding technique, it
plays a vital role in segmentation and pattern identification.
This
is a simple Approach of dividing the MRI brain image into
different parts and regions. Some grayscales values are
automatically selected as thresholds and then used in binary
classification the image into two regions: disease of Alzheimer
and not the disease of Alzheimer. System architecture of overall
proposed method is demonstrated in the below diagram.
Fig. 4. Architecture of Proposed Alzheimer's disease
detection method
Proposed method consists of two processes. In the first process,
they did shape analysis and object detection on MRI using
Contour based brain segmentation method. Drawing a proper
boundary around the skull to quantify tissue loss later on, which

11
gave us information about brain cells excluding the black
background of MRI which later on could affect the final result
and classification of disease of Alzheimer and not the disease of
Alzheimer patients.
In the second process, Binarization Technique was applied to
get White Matter count from MRI of the brain. Extracting the
count of Grey Matter was not as simple as White Matter, to get
Grey Matter count they had to invert the background of the MRI
image first and then used Binary inversion Technique to get the
count at the end (which in fact is quantification of White Matter
& Grey Matter). The segmented Grey Matter and White Matter
was analyzed to separate MRI scans of disease of Alzheimer and
not the disease of Alzheimer patients by a condition:
If count of GreyMatter > count of WhiteMatter,
It indicates the patient has Alzheimer else the patient does not

have Alzheimer
In the proposed algorithm, we used MRI brain images obtained
from Kaggle database to evaluate the calibration of the
suggested technique. Confusion Matrix is used with total 213
MRI brain scans of Alzheimer's disease patients and 256 MRI
brain scans of normal brains with accuracy: (TP + TN)/All =
93 %. Out of 238 Medical Resonance Imaging scans of
Alzheimer's disease Patients 210 were correctly identified as
Alzheimer's disease patients and 28 were miss-identified as
non-Alzheimer's disease patients, similarly out of 231 non-
Alzheimer's disease patients only 3 were miss-classified as
Alzheimer's disease patients.
Fig. 5. Confusion Matrix for Proposed Method
The existing works in the domain of Alzheimer’s disease
detection have also experimented with segmentation techniques
mentioned in detail in the literature review of paper, most of
them using similar but not identical methods discussed in this
paper. Thus, we can't say much about which method is better

than which but can compare time complexity of algorithms
which are the basis of these methods and are used in one or
another way in every paper. Time complexity using vague-c
mean is almost O(NC), K-means (Kalavathi Palanisamy, 2018)
is O(ncdi) and time complexity of FCM (Priya, 2013) is
O(ndc2i). The discussed paper used contour-based brain
segmentation method and threshold-based segmentation for
object detection, shape analysis and quantifying Grey Matter &
White Matter (segmentation of Grey Matter & White Matter).
This method is simple with low time complexity which is almost
linear depending upon the size of data and accuracy of up to
93%
in classifying (disease of Alzheimer and not the disease of
Alzheimer patients Medical Resonance Imaging images)
Medical Resonance Imaging images to the right class.
C. Artificial Intelligence in Diagnosis of Heart Diseases
With regards to infection conclusion, experts may have various
conclusions (Saba, 2012), which lead to various choices and

activities. Then again, the measure of accessible data, even for
a situation of an average infection is tremendous to the point
that
quick and accurate decision might be troublesome. For instance,
specialists may recommend a few costly tests so as to analyze a
coronary illness though a large number of those tests probably
won't be needed. Appropriately, a medical expert system
(Nazari, 2018) could be exceptionally useful there. Specifically,
such a medical expert system can be created as a specialist
framework for those persons who have a major probability of
creating heart illnesses. The investigation builds up a specialist
framework dependent on vague Systematic Ranking method and
Vague reasoning method such that as to assess the state of
persons that are getting inspected for heart illnesses. Vague
Systematic Ranking method is utilized to ascertain loads for
various standards that effect creating heart illnesses, and the
vague reasoning method is utilized for survey & assess the
probability of creating heart sicknesses in a patient. The created
framework has been executed in an emergency clinic. The

results show productivity and exactness for created approach.
The paper introduced a crossover philosophy dependent on the
Vague Systematic Ranking method and vague reasoning method
to plan a medical expert system with the point of assessing
probability of creating heart illnesses. The created medical
expert system causes professionals and masters to just
12
recommend progressed finding checks for highest probability of
creating heart illnesses has resolved. The significant advantage
of such a medical expert system is that an underlying analysis
will be led prior to endorsing any, more probable costly,
clinical
tests. Consequently, it extraordinarily decreases the related
expenses and assets, while the ideal results are destined to be
delivered.
V. CONCLUSION

A quickly developing demand to guarantee is, all wellbeing
control experts have the capacities needed to explore compound
universe of Artificial Intelligence inside the medical services
environment. Such ideas should be coordinated in all training
events which are centered on establishing Artificial Intelligence
information and abilities of doctors, medical control experts,
and administrative employees that may connect with Artificial
Intelligence advances. To be applicable and successful, safe,
and
merciful care, clinical administrations specialists must develop
the abilities to use these advancements. All the more
significantly, medical care experts possesses an occasion to
educate, formalize, and take into consideration by building up
the information, aptitudes and perspectives needed to advance
and empower Artificial Intelligence for better patient results.
The paper of diabetes innovation has introduced a unique
architecture consisting of five layers vague cosmology in order
to show the discipline information with vulnerability and stretch

out the vague metaphysics to the diabetes area. At long last, the
outcomes are put away in the diabetes decision making support.
Test results demonstrate that the proposed strategy can examine
information and further exchange the gained data into the
information to imitate the considering cycle of people. Our
outcomes further show that the proposed technique works more
adequately for diabetes application than recently created ones.
The second discussed paper of Alzheimer’s disease, proposes a
simple method, to detect Alzheimer’s disease in MR brain
images. They applied a widely used Contour based brain
segmentation method for segmentation of White Matter and
Grey Matter from brain image scans to classify either as per son
with Alzheimer or not Alzheimer infected MRI images. Their
method is a simple methodology with low time complexity and
accuracy of up to 93% in classifying the Magnetic Resonance
Imaging images to the right class. They tested the proposed
algorithm with the image dataset taken from Kaggle and found
better segmentation results than the existing methods. The
above

procedure is capable of classification into the 3 types of
Alzheimer’s disease using segmentation. For this they need to
find an accurate threshold before Binarization. Quantifying loss
of brain tissues using this technique can be used for
classification of Alzheimer’s disease. This is the future
recommendation proposed by the evaluating the above method.
It is very important to note that while the past examinations
have
constraints in the quantity of variables they considered, they
didn't research the communication between factors. In actuality,
the current investigation centers on building up an exact
medical
expert system, where the constraints of the past examinations
are survived. Also, the created medical expert system evaluates
patients' conditions at a less expensive cost in light of the fact
that extra costly conclusion and clinical tests may be
recommended once the medical expert system reports a high
probability of creating heart infections. To assess the proposed
medical expert system, they examined 100 patients. While the

masters at first recommended progressed conclusion checks for
81 persons, from which just 20 experienced heart diseases, the
medical expert system inferred that lone 26 persons, as well as
the past 20 persons, have a high probability of creating heart
infections. In synopsis, the suggested medical expert system
contributed into a generous sparing in expenses, and assets, and
in the request for some amount for a solitary patient.
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9. Elkin, P. L. (2018). Artificial intelligence: bayesian
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10. Giovanna Sannino, N. B. (2019). Artificial
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Mobile Information Systems.
11. Giuseppe Aceto, V. P. (2020). Industry 4.0 and
Health: Internet of Things, Big Data, and Cloud
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Segmentation: A Convolutional Method Applied to
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13. Jaafar, H. F.-N. (2011). Jaafar, H. F., Nandi, A. K.,
& Al-Nuaimy, W. (2011, August). Detection of
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14. Jacobs, N. S. (2020). Artificial Intelligence in
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15. Jarrahi, M. H. (2018). Artificial intelligence and the
future of work: Human-AI symbiosis in
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21. Kalavathi, P. C. (2017). Detection of Alzheimer
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14
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with Clustering.
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Clinical Guidelines and Treatment.
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Website to access books :
Keohane : https://1lib.us/book/2482110/4357c7
Northouse : https://1lib.us/book/5023149/2a539d
This course requires critical thought about the course readi ngs
and class discussions in order to critically understand and
develop your own sense of what leadership means, so the paper
will focus on your perspectives on leadership, drawing from the
course readings and outside supporting materials. The paper
assignment handout on Blackboard explains the requirements
for the paper in more detail. Papers will be evaluated according
to the evaluation form at the bottom of the paper assignment
sheet. Each paper should be 6-7 pages in length, and requires
the use of scholarly research (8-10 sources).
Purpose: The purpose of the paper is to demonstrate
understanding of leadership concepts, to compare and contrast
theories and ideas, and to apply these concepts to contemporary
leadership to better understand the function and nature of
leadership.
Assignment: Paper #1: Leadership approaches and theories.
In this paper, you will draw from different approaches as
discussed in Northouse and Keohane to develop your own

theory of leadership and applies it to your workplace or other
leadership situation. As you develop a theory of leadership,
you should clearly identify the leadership theories that inform
it, and then discuss why this theory would be helpful in your
particular leadership situation.
Background : I play football for UTEP so that’s my workplace
Im a leader/captain on the team

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