A Systematic Review for Heart Disease Prediction Using AIML Algorithms

Kummari  Jayasri; N. Satheesh  Kumar

Abstract

Cardiovascular diseases (CVDs), commonly known as heart diseases, have consistently held the position of being the primary global cause of mortality for many years. They are also the most serious illness in India and the rest of the world. So, a method that is reliable, accurate, and easy to use is needed to find these diseases early and start the right treatment. Using a variety of medical datasets, machine learning and deep learning techniques have been used to automate the analysis of large and complex data sets. In recent years, many researchers have used a wide range of methods to help doctors and other medical professionals find heart-related illnesses. This study looks at a number of models that were made using these methods and techniques, and it figures out how well they work. Researchers have a strong inclination towards models rooted in supervised learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Naive Bayes, Decision Trees (DT), Random Forest (RF), ensemble models, and various deep learning algorithms.

Abstract

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