HEART DISEASE PREDICTION USING MACHINE LEARNING

BACKGROUND

In comparison to the brain, which is the most important organ in the human body, the heart is the next most important organ. It circulates blood and supplies all of the body's organs. Heart disease ailments are the most common of all diseases. Medical experts undertake various reviews on heart diseases and compile information on heart patients, their symptoms, and disease development. Many harmful habits, such as excessive cholesterol, obesity, increased triglyceride levels, hypertension, and so on, raise the risk of heart disease. People in this fast-paced world want to live a very luxurious life, so they work like machines in order to earn a lot of money and live a comfortable life. As a result, they forget to look after themselves, and their food habits and lifestyles change as a result. They are more tense, have high blood pressure and sugar levels at a young age, and they don't give themselves enough rest and eat whatever they get.

OBJECTIVES

Objective of this project is to predict heart disease using machine learning. Also developed a web application for users using this machine learning model to predict heart disease.

DATA SOURCE

For heart disease prediction 1.0 I have collected the dataset from Kaggle. In this dataset there are 300 examples of data with 14 various attributes like age, Cholesterol, type of chest pain, electrocardiogram result, etc. In this research, we have used four algorithms to get reasons for heart disease and create a model with the maximum possible accuracy. Data like

For prediction 2.0 i use The cardiovascular disease dataset is an open-source dataset found on Kaggle. The data consists of 70,000 patient records (34,979 presenting with cardiovascular disease and 35,021 not presenting with cardiovascular disease) and contains 11 features (4 demographic, 4 examination, and 3 social history).The cardiovascular disease dataset is an open-source dataset found on Kaggle. The data consists of 70,000 patient records (34,979 presenting with cardiovascular disease and 35,021 not presenting with cardiovascular disease) and contains 11 features (4 demographic, 4 examination, and 3 social history).Data like

Used Model

For prediction 1.0 i use KNN and For prediction 2.0 i use RandomForest because i got best result in this Model .

Note : This disease predicton system is not 100% correct. If you have any health related issue please consult with your doctor about it.
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