An integrated machine learning and fractional calculus approach to predicting diabetes risk in women

An integrated machine learning and fractional calculus approach to predicting diabetes risk in women

This study presents a novel dual approach for diabetes risk prediction in women, combining machine learning classification with fractional-order physiological modeling. We employ seven machine learning algorithms: Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest, Bagge...

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Bibliographic Details
Main Authors: David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Healthcare Analytics
Subjects:
Machine learning
Fractional modeling
Diabetes prediction
Physiological dynamics
Risk assessment
Women health
Online Access:http://www.sciencedirect.com/science/article/pii/S2772442525000218
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