A survey of knowledge acquisition techniques and tools
Knowledge Acquisition
Comparing association rules and decision trees for disease prediction
HIKM '06 Proceedings of the international workshop on Healthcare information and knowledge management
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Intelligent heart disease prediction system using data mining techniques
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Discrimination of myocardial infarction stages by subjective feature extraction
Computer Methods and Programs in Biomedicine
Assessment of the risk factors of coronary heart events based on data mining with decision trees
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data
Knowledge-Based Systems
Automated Screening of Arrhythmia Using Wavelet Based Machine Learning Techniques
Journal of Medical Systems
Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling
IEEE Transactions on Information Technology in Biomedicine
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Interval type-2 fuzzy logic for encoding clinical practice guidelines
Knowledge-Based Systems
Hi-index | 0.00 |
Coronary artery disease (CAD) affects millions of people all over the world including a major portion in India every year. Although much progress has been done in medical science, but the early detection of this disease is still a challenge for prevention. The objective of this paper is to describe developing of a screening expert system that will help to detect CAD at an early stage. Rules were formulated from the doctors and fuzzy expert system approach was taken to cope with uncertainty present in medical domain. This work describes the risk factors responsible for CAD, knowledge acquisition and knowledge representation techniques, method of rule organisation, fuzzification of clinical parameters and defuzzification of fuzzy output to crisp value. The system implementation is done using object oriented analysis and design. The proposed methodology is developed to assist the medical practitioners in predicting the patient's risk status of CAD from rules provided by medical experts. The present paper focuses on rule organisation using the concept of modules, meta-rule base, rule address storage in tree representation and rule consistency checking for efficient search of large number of rules in rule base. The developed system leads to 95.85% sensitivity and 83.33% specificity in CAD risk computation.