An Intelligent Heart Disease Identification Framework Using Supervised Machine Learning for Clinical Decision Support

Sujit Shibaprasad Maity

doi:10.36948/ijfmr.2026.v08i01.69115

An Intelligent Heart Disease Identification Framework Using Supervised Machine Learning for Clinical Decision Support

Author(s)	Mr. Sujit Shibaprasad Maity
Country	India
Abstract	Cardiovascular-diseases (CVDs) remain the leading cause of global mortality, necessitating early and reliable diagnostic support systems. Traditional diagnostic procedures, while clinically effective, are time-intensive and dependent on expert interpretation. Recent advances in machine learning (ML) have demonstrated significant potential in automated medical decision support systems [9], [29]. This study proposes a supervised machine learning-based framework for intelligent heart disease identification using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Six classification algorithms—Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, Support Vector Machine (SVM), and Random Forest—are systematically evaluated under stratified k-fold cross-validation. Experimental findings demonstrate that the Random Forest classifier achieves superior predictive performance with an accuracy of 87%, alongside strong sensitivity and specificity. Model robustness is validated through confusion matrix analysis, Receiver Operating Characteristic (ROC) curves, Precision–Recall evaluation, and statistical significance testing (p < 0.05). Feature importance analysis further enhances interpretability by identifying clinically relevant predictors. The proposed framework demonstrates strong potential as a decision-support tool in e-healthcare systems while emphasizing that it complements, rather than replaces, clinical expertise.
Keywords	cardiovascular disease prediction, supervised machine learning, Random Forest; ensemble learning, clinical decision support systems, e-healthcare analytics, medical data mining, predictive modeling
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-17
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.69115

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An Intelligent Heart Disease Identification Framework Using Supervised Machine Learning for Clinical Decision Support

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