Heart Disease Prediction Using Machine Learning

Abstract

In the medical field, diagnosing heart disease is one of the most challenging tasks. It relies on analyzing large sets of clinical and pathological data, making the process complex. As a result, there has been a growing interest among researchers and clinical professionals in developing efficient and accurate methods for heart disease prediction. Early diagnosis of heart disease is crucial due to its potential severity, as timely intervention can significantly impact patient outcomes. Heart disease is a leading cause of death globally, underscoring the importance of predicting it at an early stage. In recent years, machine learning has emerged as a reliable tool in the medical domain, offering valuable support in disease prediction when provided with appropriate training and testing data. A range of machine learning algorithms, encompassing decision trees, support vector machines, random forests, neural networks, and ensemble methods, have been employed to analyze varied datasets containing demographic information, clinical parameters, and medical imaging data. To improve prediction accuracy and interpretability, techniques for feature selection and model Optimizations have been utilized. The primary objective of this study is to explore various prediction models for heart disease and identify key features using the Random Forest algorithm. Random Forest is a supervised machine learning algorithm known for its high accuracy compared to other methods such as logistic regression. Utilizing the Random Forest algorithm, we aim to predict whether an individual has heart disease or not