Asthma is a common chronic disease that affects millions of people in the world. The most common signs and symptoms of asthma are cough, breathlessness, wheeze, chest tightness and respiratory rate. These signs and symptoms can’t be measured accurately since they consist of various types of uncertainties such as vagueness, imprecision, randomness, ignorance, incompleteness. Consequently, traditional disease suspicion, which is carried out by the physician, is unable to deliver accurate results. Hence, this paper presents the design, development and application of a decision support system to assess asthma suspicion under uncertainty. Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach (RIMER) was adopted to develop this expert system that is named as Belief Rule Based Expert System (BRBES). The system has the capability to handle various types of uncertainties both in knowledge representation and inference procedures. The knowledgebase of this system was constructed by taking account of real patient data and expert’s opinion. The practical case studies were used to validate this system. It was observed that the system generated results are more effective and reliable in terms of accuracy than the results generated by a manual system.

1. A BELIEF RULE BASED (BRB) DECISION
SUPPORT SYSTEM TO ASSESS CLINICAL
ASTHMA SUSPICION
MOHAMMAD SHAHADAT HOSSAINA, MD. EMRAN HOSSAINB,
MD. SAIFUDDIN KHALIDC, MOHAMMAD A. HAQUED
A, BDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, UNIVERSITY OF CHITTAGONG, BANGLADESH
CDEPARTMENT OF LEARNING AND PHILOSOPHY & DDEPARTMENT OF ARCHITECTURE, DESIGN AND MEDIA
TECHNOLOGY, AALBORG UNIVERSITY, DENMARK
SCANDINAVIAN CONFERENCE ON HEALTH INFORMATICS (SHI 2014), 21-22 AUGUST

2. Presentation Outline
 Aims and Objectives
 Asthma & Related Works
 Belief Rule Base (BRB)
 BRB System to Assess Clinical Asthma Suspicion
 Result & Discussion
 Conclusion

3. Aims and Objectives
 Signs, Symptoms and Uncertainties
 Causal relationships between signs and symptoms – representation
by If-Then rule
 Drawbacks of methodologies and algorithms
 Expert system: Belief Rule-Based Inference Methodology Using the
Evidential Reasoning (RIMER)
 Work-in-progress: Optimal learning model (machine learning – to
add experience of experts on real time basis.

4. Asthma and Related Works
 Asthma is a common chronic inflammatory disease of the airways
characterized by variable and recurring symptoms, reversible airflow
obstruction, and bronchospasm.
 The most common signs and symptoms are- Cough 2)
Breathlessness (Shortest of Breath) 3) Wheeze 4) Chest tightness 5)
Respiratory Rate.
 Existing tools and methods:
 Optical breath sensor, proportional logic (PL), first-order logic (FOL) or
fuzzy logic (FL), forward chaining and backward chaining for inference
engine
 Scope: RIMER for a refined knowledge base and an inference mechanism.

5. Domain Knowledge Representation using BRB
Belief Rule

6. Domain Knowledge Representation using BRB
(Cont.)
BRB System Prototype

7. Domain Knowledge Representation using BRB
(Cont.)
Inference Procedure
Five input antecedents: cough (A1),
breathlessness (A2), wheezing (A3), chest
tightness (A4) and respiratory rate (A5).
Three referential values of these antecedent
attributes: severe (S), moderate (Mo), mild (M)
and normal (N).
Asthma (A6) has (2*4*3*2*2) = 96 belief rules

8. Domain Knowledge Representation using BRB
(Cont.)

9. The BRBES system architecture

10. BRBES Interface

11. Asthma diagnosis by BRBES and expert

12. Results and Discussion (Cont.)
The AUC for the
BRB system
prototype is 0.952
(95% confidence
interval = 0.960–
1.012), and the
AUC for the expert
opinion is 0.857
(95% confidence
interval = 0.939–
1.014).

13. Conclusion
 Reduce the medical error and various types of uncertainties
 Reduce medical cost
 BRB System employed a novel methodology known as RIMER
allows the handling of various types of uncertainty
 Currently, an attempt has been undertaken to enhance the
system with the capability to supporting the diagnosis of Asthma
 Training Module for BRB

14. THANK YOU
SCANDINAVIAN CONFERENCE ON HEALTH INFORMATICS (SHI
2014), 21-22 AUGUST