Heart Disease Prediction Using Machine Learning

Heart disease continues to be a major global cause of death, highlighting the urgent need for more effective methods of early detection and risk evaluation. This paper explores the use of machine learning techniques for predicting heart disease, focusing on five key algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. Through a comprehensive review of existing research and data, we assess the performance of these algorithms in heart disease risk prediction. The analysis reveals that machine learning approaches offer substantial improvements in both accuracy and efficiency over traditional diagnostic techniques. Among the algorithms studied, Random Forest showed the best overall results, with some studies indicating accuracy rates as high as 95% in detecting potential heart disease cases. This review underscores the transformative potential of machine learning in reshaping cardiovascular healthcare through more individualized risk assessments and the promotion of early intervention strategies. Incorporating these advanced predictive models into routine clinical workflows could lead to significantly better patient outcomes and help alleviate the worldwide impact of heart disease. Keywords: Cardiovascular Risk Assessment, Machine Learning Models, Health Data Analytics, Random Forest, Neural Networks, Feature Significance, Decision Support Systems, Precision Medicine, Predictive Healthcare Analytics, Early Diagnosis.