Heart Disease Prediction using Machine Learning Techniques

Sharyu U. Kamble, Vaishnavi S. Jawanjal, Pooja P. Velapure, Priya K. Jadhav, Sanjivani S. Kadam


Diseases related to Heart i.e. Cardiovascular Dis-eases (CVDs) are the main reason for the number of deaths in the course of the most recent couple of decades and has developed as the most perilous ailment, in India and in the entire world. In this way, there is a need for accurate, feasible and reliable system to analyze such maladies in time for legitimate treatment.

 Machine Learning algorithms and procedures have been im-plemented to various medical datasets to various medical datasets to investigate of extensive and complex information. Numerous analysts, as of late, have been using several methods to enable the health care industry and the professionals in the diagnosis of heart related diseases.

This paper demonstrates a survey of various models based on such algorithms and techniques and analyze their perfor-mance. Models depend on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, Decision Trees (DT), Random Forest (RF) and ensemble models are discovered extremely prominent among the researchers.

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