Paper Key : IRJ************175
Author: Ansh Shukla
Date Published: 18 Oct 2023
Abstract
Artificial Intelligence techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These classifying techniques are very effective in designing clinical support systems due to their ability to get hidden patterns and relationships in medical data provided by medical professionals. One of the most important applications of such systems is in the diagnosis of heart diseases because it is one of the leading causes of deaths all over the world. Almost all systems that predict heart diseases using clinical dataset having parameters and inputs from complex tests conducted in labs. None of the systems predicts heart diseases supporting risk factors like age, case history, diabetes, hypertension, high cholesterol, tobacco smoking, alcohol intake, obesity or physical inactivity, etc. Heart disease patients have many of those visible risk factors in common which may be used very effectively for diagnosis. A system based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before the patient visits a hospital or goes for costly medical checkups. Hence this paper presents a technique for prediction of heart disease using major risk factors with help of different Classifying Algorithms. This technique involves four major classification algorithms such as K Neighbours, Support Vector, Decision Tree, Random Forest algorithms.