Virtual Consultation And Machine Learning Techniques For Heart Disease Prediction

Abstract

Predicting cardiovascular disease, often known as heart disease, is among of medical field's most challenging & time-consuming endeavors. These days, cardiovascular disease is the leading cause of death, killing within 60 individuals hourly. Data science serves crucial role in medical platform by processing large amounts of data; this is especially true for cardio-vascular illness, prediction of which has to be quick & simple so that patients may be warned of potentially fatal complications as soon as possible. This work compiled patient datasets from kaggle. Using data mining methods including Logistic Regression, Decision Tree, Random Forest, & SVM suggested system of this research determines if person has a heart attack or not. There have been too many instances when you or someone else needed immediate medical attention, but for some reason you could not. Heart Disease Prediction Application is a support and online consultation application for end users. Here, we proposed heart disease prediction application that allows users to receive guidance from the heart through the process of machine learning algorithms. This application can also be used for free online heart disease counselling and the system acts as a decision support system and proved to be an aid for the physician for the diagnosis with the help of the data set.