Advancing Cardiovascular Health Prediction: Machine Learning Algorithm Analysis

Abstract

In this study focused on heart disease prediction using machine learning, an analysis of three key algorithms- Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN)- was conducted utilizing a dataset comprising information from 1025 patients. The outcomes revealed the Random Forest algorithm's exceptional performance, achieving a flawless 97.24% prediction accuracy, while Logistic Regression demonstrated strong predictive ability at approximately 87.80%. KNN showcased moderate predictive power with an accuracy of around 74.63%. These findings underscore the potential of machine learning, particularly Random Forest, in aiding heart disease prediction, emphasizing the importance of algorithm selection based not only on accuracy but also on interpretability and practical implementation within healthcare contexts, aiming to enhance diagnostic precision and patient care.

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