A SURVEY: HEART DISEASE PREDICTION USINGMACHINE LEARNING TECHNIQUES

Abstract

This Heart attacks and the deaths caused by heart diseases are increasing at every part of the world. Heart troubles are caused by people's lives being under more stress. One of the most important organs in our body is probably the heart. It assists in controlling blood circulation and flow to all of the body's organs. Today, this disease is responsible for the greatest number of fatalities. Generally a person suffering from any kind of heart problem shows few signs and symptoms from which we can easily judge it and thus can help the individual on time. The symptoms are like high intensity chest pain, fast rate of heartbeat, and pain with some uneasiness in breathing are recorded. This information is dissected on customary premise. In this survey, an outline of the coronary illness furthermore, its present techniques is right off the bat presented. Besides, a top to bottom examination of the most important AI methods accessible on the writing for coronary illness forecast is momentarily expounded. Various machine learning algorithms that performs well in every detection mechanism are Naive Bayes, Decision Tree, Support Vector Machine, Artificial Neural Network, Random Forest, K-Nearest Neighbours and many more. The performance of every machine learning model depends on how accurately it makes predictions. Therefore, one of the key components of evaluating the model's efficiency is accuracy. In our research, Random Forest provides the best accuracy of 91% in predicting heart disease.