A Self-Learning Fuzzy Rule-based System for Risk-Level Assessment of Coronary Heart Disease

摘要

Automation of intelligent behaviours such as learning, reasoning with improved accuracy helps in early prediction of disease and assists clinical experts in immediate treatment planning. Coronary heart disease is indubitably the commonest manifestation of cardiovascular disease. Even though intelligent systems have been developed to predict the severity of coronary heart disease, an efficient approach is required to handle the uncertainties in clinical data. A self-learning fuzzy rule-based system has been developed for early detection of coronary disease by assessing risk level of individuals. It achieved an overall accuracy of 90.7% and provided encouraging results when compared with other techniques.