COMPARATIVE STUDY ON MACHINE LEARNING ALGORITHMS FOR PREDICTING HEART ATTACK AMONG HIGH BP

Abstract

India has the world’s largest youth population and large English speaking workforce of engineers, doctors and other professionals, many youngsters in new economy sectors, invites risks by aggressively pursuing tall work targets and developed stress. The major crisis faced by healthcare today is raising tide of non-communicable diseases (NCD). Especially cardiovascular ailment, 54% chances are with high blood pressure (hypertension) to suffer from any heart related problems. In this paper we have analysed the different prescribed dataset of containing 303rows with 14columns.However the accuracy of classification model will be reduced when the quality of medical data is incomplete. we have used different missing values interpolation techniques to reconstruct the missing data, and analysed in which age there are more chances of getting heart attack with certain parameters and compared seven algorithms (logistic regression,decisiontree, randomforest,,Xgboost, Ada boost, K Neighborclassifier,SVM) to get the accuracy results. Machine learning can play a major part in the prediction and monitoring of disease while empowering physicians with the power of data to make superior clinical decision.