Feature Diminution by Using Particle Swarm Optimization for Envisaging the Heart Syndrome

Health Ecosystem is derisory in techniques to haul out the information from the database because of the lack of effective scrutiny tool to discern concealed relationships and trends in them. By applying the data mining techniques, precious knowledge can be excerpted from the health care system. Extracted knowledge can be applied for the accurate diagnosis of disease and proper treatment. Heart disease is a group of condition affecting the structure and functions of the heart and has many root causes. Heart disease is the leading cause of death in all over the world in recent years. Researchers have developed many data mining techniques for diagnosing heart disease. This paper proposes a technique of preprocessing the data set and using Particle Swarm Optimization (PCO) algorithm for Feature Reduction. After applying the PCO, the accuracy for prediction is tested. It is observed from the experiments, a potential result of 83% accuracy in the prediction. The performance of PCO algorithm is then compared with Ant Colony Optimization (ACO) algorithm. The experimental results show that the accuracy obtained from PCO is better than ACO. The performance measures are based on Accuracy, Sensitivity and Specificity. The other measures such as Kappa statistic, Mean Absolute Error, Root Mean Squared Error, True Positive Rate are also taken for evaluation. As future direction of this paper, a hybrid technique which combines PCO with Rough Set theory is suggested.