Heart Disease Prediction using Machine Learning and Data Mining

Abstract

- Heart disease detection is the need of great importance as it weakens grown-ups as well as demonstrating manifestations, all things considered, over the world. This can happen to the individual, and who has an inappropriate eating routine, elevated cholesterol level and smoking propensities, dependence on liquor or sedates and even happens to a diabetic patient. Different methodologies are there in different fields, state in Machine Learning, Soft Computing and Data Mining. Weighing only 300 grams where the Heart is declining the mortality rate at a rapid pace from decades. Even with this much technical advancements the analysis of the clinical data disease is a critical challenge. By the using Machine Learning techniques, it is possible to analyze the data and interpret the cause that led to heart diseases like Coronary Heart Disease, Arrhythmia, and Dilated Cardiomyopathy. Many of the researchers who are developing IoT enabled hardware for predicting these diseases using various ML techniques. Here we propose various methods to detect the heart disease using Cleveland Heart Disease Dataset by combining the computational power of various ML algorithms and Along with this, and a web application is developed by using flask in python language where the user can enter the attributes and predict that he has heart disease or not.