A Decision Tree – Based Heart Disease Prediction Model with SMOTE and Fractional-Order Feature Transformation

Abirami. M; Chandrasekaran. S

doi:https://doi.org/10.14445/22315373/IJMTT-V72I9P103

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 72 | Issue 9 | Year 2026 | Article Id. IJMTT-V72I9P103 | DOI : https://doi.org/10.14445/22315373/IJMTT-V72I9P103

A Decision Tree – Based Heart Disease Prediction Model with SMOTE and Fractional-Order Feature Transformation

Abirami. M, Chandrasekaran. S

Received	Revised	Accepted	Published
13 Jul 2026	19 Aug 2026	12 Sep 2026	29 Sep 2026

Citation :

Abirami. M, Chandrasekaran. S, "A Decision Tree – Based Heart Disease Prediction Model with SMOTE and Fractional-Order Feature Transformation," International Journal of Mathematics Trends and Technology (IJMTT), vol. 72, no. 9, pp. 45-55, 2026. Crossref, https://doi.org/10.14445/22315373/IJMTT-V72I9P103

Abstract

This study compares two machine-learning pipelines for heart disease prediction using lipid-profile variables: Decision Tree with Synthetic Minority Oversampling Technique (SMOTE) and fractional-order feature transformation followed by Decision Tree with SMOTE. A truncated Grünwald–Letnikov-style transformation with fractional order α = 0.5 was used to construct an alternative feature representation before classification. The study evaluates accuracy, balanced accuracy, precision, sensitivity, F1-score, ROC-AUC, training time, and Decision Tree complexity. The conventional Decision Tree + SMOTE model achieved higher predictive performance on the analyzed dataset, with an accuracy of 87.96% and ROC-AUC of 0.922, whereas the fractional-order model achieved lower predictive values but a shallower tree and slightly shorter training time. The findings indicate that the fractional-order transformation changed the feature representation and tree structure but did not provide a predictive advantage under the experimental conditions considered. The contribution is therefore an empirical comparison of fractional-order feature representation with a conventional SMOTE-based Decision Tree pipeline for lipid-profile-based heart disease prediction.

Keywords

Decision tree, Fractional – Order Feature Transformation, SMOTE, Heart Disease, ROC-AUC.

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