A Metaphoric Investigation on Prediction of Heart Disease using Machine Learning

Abstract

Nowadays, heart diseases are considered as the biggest concern in the field of healthcare. Heart diseases mostly lead to death when a patient gets a heart attack. Most of the times, it becomes difficult for the medical practitioner to accurately identify the presence of heart disease with a particular patient. If the disease can be identified at an early stage then it becomes easy to cure it. As medical diagnosing is a decision-making technique, an intelligent decision system can be implemented by using various machine learning classification models which will help the medical practitioner to accurately diagnose the heart disease. In this survey, we have analysed the performance of various heart disease prediction techniques, namely ABC-SVM, ANFIS, SVM-ANN, SVM-SSVM, Genetic Algorithm, Neural Network Ensemble, FNN, Majority Vote Based Ensemble Classifier etc. All these techniques have used Cleveland Heart Disease dataset of UCI Machine Learning Repository.

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