Integrating Artificial Intelligence and Hybrid Soft Sets for Heart Disease Prediction

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DOI: https://doi.org/10.28924/2291-8639-24-2026-146
Published: May 11, 2026
Cite as:
Rehab Alzhrani, Sara Alshehri, Noura Alshehri, Integrating Artificial Intelligence and Hybrid Soft Sets for Heart Disease Prediction, Int. J. Anal. Appl., 24 (2026), 146.

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Rehab Alzhrani, Sara Alshehri, Noura Alshehri

Abstract

This paper explores the integration of Artificial Intelligence (AI) and Hybrid Soft Sets (HSS) for heart disease prediction and risk stratification. By combining the predictive accuracy of Random Forest models (accuracy: 86.67%, F1 Score: 87.23%) with the interpretability of HSS, the study provides a robust framework for analyzing medical data. Hybrid Soft Sets enhance transparency through fuzzified decision rules, categorizing patients into risk levels based on attributes like age and cholesterol. This approach bridges the gap between AI’s complexity and clinical interpretability, offering a scalable, explainable solution for real-world healthcare applications.

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