A Comparative Analysis on the Prediction of Heart Failure using Machine Learning Algorithms
Senthil Pandi S, T Kumaragurubaran, M Jaeyalakshmi, Muqaddam Aaqil Sheriff
Abstract
The primary signs and symptoms of cardiovascular diseases, which kill over 17 million people yearly, are heart failure and myocardial infarctions. When the heart cannot pump enough blood to meet out the body’s needs, Heart Failure (HF) results. The solution proposed in the following sections, firstly it states the primary reasons for heart failure and the major causes behind it. Furthermore, it talks about the solutions which help doctors find out the risk of heart failure in a particular individual with the help of the below discussed parameters. The parameters are crucial from a biological point of view. The way of exploring if it is at risk or lot lies with machine learning. A cardiovascular disease affects millions of people worldwide and is a serious and expanding health concern. This research study integrates data from several patient demographics to provide a thorough overview of heart failure. This research provides study’s prevalence of heart failure, with findings in line with previously published epidemiological data. Odds ratios were used to identify and quantify risk variables, such as diabetes and hypertension. A Data set it used to find out the accuracy using 3 machine learning algorithms. The data set contains 14 attributes which are extensively used by machine learning researches.