Smart Machine Learning-Based Heart Disease Prediction with Random Forest Classifier
Received: 7 February 2026 | Revised: 6 March 2026, 24 March 2026, 27 March 2026, 29 March 2026, 1 April 2026, and 3 April 2026 | Accepted: 4 April 2026 | Online: 4 June 2026
Corresponding author: Sujatha Krishna
Abstract
Heart diseases remain one of the most common causes of death worldwide, indicating a demand for reliable and accurate early prediction systems. In contrast to established comparative studies, this one develops a unique data-driven predictive framework that encompasses multiple Machine Learning (ML) algorithms and an optimized Random Forest (RF)-based classification strategy for enhanced clinical decision-making support. A systematic heart disease prediction model was designed, based on Logistic Regression (LR), Gaussian Naïve Bayes (GNB), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Decision Tree (DT), Extra Trees (ET), Bagging, and an optimized RF classifier aimed at increasing predictive performance stability and generalizability. Experimental results show that RF achieves better performance with 97.50 accuracy, 97.51 precision, 97.50 recall, and 97.50 F1-score on the validation dataset. The proposed model integrates ensemble-based optimization and effective feature learning and generalization ability for medical risk prediction. Its better performance is due to the ensemble learning mechanism of RF, which increases model robustness, helps to decrease overfitting, and increases classification reliability. With its ability to accurately direct clinicians and health organizations to unknown patients at risk, the proposed model can offer great value in clinical and health economy perspectives, through timely intervention for both diagnosis of imminent heart disease complications and varied data-informed prescription steps.
Keywords:
heart disease prediction, Random Forest (RF), Machine Learning (ML), medical diagnosticsReferences
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Copyright (c) 2026 Sujatha Krishna, Pandian Vaidhyanathan, Amina Salim Mohammed AlJabri, Amna Salim Rashid Al Kaabi
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