Data Mining, Soft Computing, Machine Learning and BioInspired Computing for Heart Disease Classification/ Prediction – A Review

M. Rathi, B. Narasimhan
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
Data mining is the most common research area in the field of computer science and allied areas. Decision making in clinical data mining plays a significant role in patient's life. In this survey research article we aim to portray various data mining algorithms, soft computing techniques, machine learning algorithms and bio-inspired algorithms for predicting / classifying heart disease. Several mechanisms namely apriori algorithm, frequent itemset mining, support vector machine, neural network,
more » ... lassification and regression trees, fuzzy rule-based clinical decision support system, k-nearest neighbor, genetic algorithm, scoring system, nature language processing (NLP) techniques, type-2 fuzzy logic system, decision tree and statistical methods are used to classify heart disease prediction. From this survey research, it is identified that support vector machine algorithm outperforms all the other methods in terms of classification accuracy.
doi:10.23956/ijarcsse/v7i4/0156 fatcat:6umrnxncxve55ejgzpkhaeoxpy