Prediction of Heart Disease using Machine Learning Algorithms

Abstract

For medical purposes, the diagnosis of heart sickness is one of the difficult ventures. A known range of available datasets leads to numerous options with the help of analyzing the data and visual analytics. In this proposed work we have analyzed the features for which we can implement a great system from a large amount of available data. In the absence of useful information, massive statistics can lead to vague outcomes. It relies upon careful evaluation of various medical and pathological facts of the sufferers obtained through health workers, in a complex manner. For validating the performance of proposed work, it applied on complete traits and on decreased bunch of traits. Classifier performance is controlled by feature reduction with respect to precision and implementation time of classifier. Doctors can be assisted in diagnosing heart patients effectively through machine learning. So, study has been made of heart sickness for all age group sufferers by assistance of machine learning algorithms. Decision tree algorithm has been used to make the predictions whether a person has heart sickness or not followed by the Ada-Boost algorithm. Among the applied classifier importance of features have been identified.