PERFORMANCE ANALYSIS OF SOFT COMPUTING TECHNIQUES TOWARDS HEART DISEASE DIAGNOSIS SYSTEM

Abstract

Data mining is the extraction of concealed prescient data from expansive databases furthermore a capable of new innovation with incredible potential to examine critical data in their data warehouses. Data mining algorithms anticipate future patterns and behaviors, permitting organizations to make proactive and knowledge driven choices. The computerized, forthcoming analyses offered by data mining move beyond investigations of past occasions gave by review tools commonplace of choice emotionally supportive networks. Data mining algorithms can answer business addresses that customarily were excessively prolonged to determine. They scour databases for concealed examples, discovering the prediction of disease that specialists may miss in light of the fact that it lies outside their desires. Manual checking is highly impossible to diagnose for this disease. To predict heart disease several approaches have been carried out. This comparative study paper provides a thorough analysis of various algorithms made towards disease prediction. Several data mining and soft computing approaches are studied. This study concludes that the performance of various algorithms comparison of accuracy, sensitivity and specificity of several algorithms and approaches.