Machine Learning Based Cardiovascular Disease Prediction

Abstract

The ability to predict a heart attack at an early stage is extremely difficult nowadays. Although there are numerous heart attack detection devices, applying machine learning has a greater level of accuracy. When used in the healthcare industry, machine learning has the potential for greater accuracy and early diagnosis of diseases. The heart disease illness circumstances that may arise will be computed. Medical parameters are characteristics of the data sets used. The data-sets are processed using machine learning Algorithm in Python. This method makes use of historical patient records from the past to forecast future ones at an early stage, saving lives. Our objective is to predict heart disease by processing patient data sets and data of patients, i.e., users whom we need to predict the likelihood of a heart disease occurrence. Prediction is one area where machine learning plays a crucial role. Robust machine-learning algorithms will be used to construct an accurate heart disease prediction system. In this regard, Logistic regression and Artificial Neural Network based techniques have been discussed in predicting cardiovascular disease. The proposed techniques were further validated in terms of accuracy during cardiovascular disease prediction