HEART DISEASE PREDICTION USING MACHINE LEARNING

Abstract: Heart disease is one of the leading causes of death worldwide. Early diagnosis and prediction can play a vital role in preventing life-threatening conditions. The traditional methods for predicting heart disease are often manual, time-consuming, and prone to errors. In this research, a machine learning-based model is proposed to predict the likelihood of heart disease based on clinical data such as age, gender, blood pressure, cholesterol, and other medical attributes. Various algorithms like Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) were implemented and compared. The dataset used was the UCI Heart Disease Dataset. The results show that Random Forest Classifier achieved the highest accuracy of 88.5%, making it a reliable model for real-world applications. The study aims to assist medical practitioners in making better and faster diagnostic decisions.

Keywords: Heart disease prediction, Data mining, Risk factors, Feature selection, Real-world healthcare data, Neural network, Deep learning