Heart Attack Risk Analysis and Estimation Using Machine Learning Methods

Abstract

Heart disease is a disease that is difficult to diagnose and leaves serious damage to individuals like many other diseases today. It is not known whether the risk of this disease is carried or not, and it is observed that there is an increase in the number of individuals at risk today. This increase; It requires accelerating the diagnosis of the disease to humanity by making early intervention and risk analysis together with developing technologies. Machine learning methods are developing rapidly in this field, facilitating early diagnosis in medicine. Diagnosing the disease with the developed methods provides a great advantage in terms of time cost. With the developments made, the diagnosis of diseases related to more than one parameter is carried out in a very short and reliable way. In this study; with the dataset consisting of the parameters and values of carrying the risk of heart attack, the classification of the risk of heart attack with high / low probability was made using Logistic Regression, which is one of the machine learning methods. By referring to what the parameters are, the distribution and values of these parameters on the dataset are determined. Obtained values; The effect of the parameters on the result status was analyzed using visualization methods. The main purpose of these analyzes is to determine the need for corrections on the dataset before training the network. As a result of the experimental analysis, 97% overall accuracy was achieved with the proposed approach.