A Survey on Heart Disease Prediction using Composite Machine Learning Algorithms

The heart is the bodily part that is most necessary or significant. Our body's blood must be combined and controlled by the heart. Around the world, heart disease is a problem that many individuals face. Many individuals die as a result of heart disease. Numerous symptoms are mentioned, including chest discomfort and an irregular heartbeat. A reliable, precise, and useful strategy is required to identify these disorders early enough for appropriate therapy. For medical experts and institutions throughout the world, especially hospitals in India, forecasting and recognizing heart disease has become a challenging challenge. The researchers are targeting the development of software employing machine learning techniques to help physicians with the identification and prognosis of cardiac disease. The algorithms are used based on characteristics to anticipate heart illness. The effectiveness of various models built using such methodologies and techniques is investigated in this study. Models created utilizing supervised learning techniques like Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), etc. are frequently preferred by researchers. The primary goal of this study is to use machine learning algorithms to better correctly predict a patient's heart state