An ensemble based decision support framework for intelligent heart disease diagnosis

Large amount of medical data leads to the need of intelligent data mining tools in order to extract useful knowledge. Researchers have been using several statistical analysis and data mining techniques to improve the disease diagnosis accuracy in medical healthcare. Heart disease is considered as the leading cause of deaths worldwide over the past 10 years. Several researchers have introduced different data mining techniques for heart disease diagnosis. Using a single data mining technique shows an acceptable level of accuracy for disease diagnosis. Recently, more research is carried out towards hybrid models which show tremendous improvement in heart disease diagnosis accuracy. The objective of the proposed research is to predict the heart disease in a patient more accurately. The proposed framework uses majority vote based novel classifier ensemble to combine different data mining classifiers. UCI heart disease dataset is used for results and evaluation. Analysis of the results shows that the sensitivity, specificity and accuracy of the ensemble framework are higher as compared to the individual techniques. We obtained 82% accuracy, 74% sensitivity and 93% specificity for heart disease dataset.