Explored Research on Data Preprocessing and Mining Technology for Clinical Data Applications

1. Explored Research on Data Preprocessing and Mining
Technology for Clinical Data Applications
GROUP MEMBERS
1. S.M.ZAHIDUL ISLAM(152-15-5925)
2. MD.HABIBUR RAHMAN(152-15-6040)
3. AKTERUZZAMAN(152-15-5671)
4. ABU MOHAMMAD MUSA(152-15-5682)

2. Overview
 Abstract
 INTRODUCTION
 DATA PREPROCESSING
 DATA MINING
 CLINICAL MEDICAL DATA
 BIOCHEMICAL INDICATORS
 DATA PREPROCESSING AND NETWORK MODELING
 CONCLUSION AND DISCUSSION

3. Abstract
Clinical Medical
Data
Disease
Prediction
Preprocessing
Self-organizing
Feature Maps
Clinical
Diagnosis

4. INTRODUCTION
People felt
uncomfortable for they
would go to hospital.
Development area
(data mining technology,
information science,
improvement of HIS)
Hidden essence
relationships
Combining information
science with clinical
medical data.

5. Data Processing
 Design defects of medical equipment
 Subjective factors
 Experimental environment force majeure
 Noise
 Redundancy
 Mistakes

6. Integration &
Association
Cleaning
Filling & De-noising
Normalization Input of Network
Flow Chart of Processing

7. Data Selection
Processing
Clustering &Mining
Analysis &
Explanation
Trend analysis and
disease intervention
Modeling

8. CHARACTERISTICS OF CLINICAL
MEDICAL DATA
Clinical
Medical
Data
1.
Privacy.
2.
Diversity.
3.
Complexit
y.
4.
Incomplet
eness.
5.
Redundan
cy.
6.
Heteroge
neity.

9. CHARACTERISTICS OF BIOCHEMICAL
INDICATORS
1.Single Source
2.Different Types of Raw
Data
3.Increased difficulty of Data Mining &
Importance of Data Preprocessing
4.Huge Data resource(Database)

10. Data preprocessing and network modeling
1. Data Preprocessing: It is quality assurance of data source, which can
greatly improve the accuracy and performance of dataminin.
2. Data Mining: It is an information processing technology .
 knowledge from database
 Artificial intelligence
 statistics and pattern recognition
 Visualization technology and parallel computing ect.

11. Flow chart
Integration &
Association
Cleaning
Filling & de-
noising
Normalization
Input of Network
Bio chemical indicator
Data
selection
Preprocessin
g
Clustering
&mining
Making use of
biological
computational
methods, statistical
theory, pattern
recognition and
artificial intelligence in
data mining, at present
SOM network is
adopted .
Analysis &
explanation
Feedback
Adjustment
Information Obtainment of
Health
Assessment, Trend Analysis
and Disease Intervention

12. Conclusion & Future work
 SOM does not need training set after adjusting learning function of weight
and threshold.
 Base for future study on early warning and intervention of health status.
 Provide greatest convenience and objective benefit for assisting doctor
diagnosis and medical decision management
 Introduce manifold learning of geo-information science into data mining
on clinical data
Problem: how to determine slow changing part around peak is its sub-
classification or a new classification .

13. THANKS