Enhancing Diabetes Risk Prediction with Hybrid Machine Learning Models

Sahar Echajei (), Hanane Ferjouchia and Mostafa Rachik
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Sahar Echajei: Ben M’sik - Hassan II University of Casablanca
Hanane Ferjouchia: Ben M’sik - Hassan II University of Casablanca
Mostafa Rachik: Ben M’sik - Hassan II University of Casablanca

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 310-318 from Springer

Abstract: Abstract This paper explores the integration of causal inference with machine learning (ML) to enhance early diagnosis and effective management of diabetes. By leveraging advanced techniques such as data preprocessing, causal analysis, evaluation of variable importance, feature engineering, and hyperparameter optimization, we develop a predictive model using a Stacking ensemble that combines multiple base models. Initial results demonstrate significant improvements in model performance, suggesting that this integrated approach offers a promising direction for diabetes management.

Keywords: Machine Learning; Classification; Ensemble Technique; Bayesian Networks; Causal Inference; Diabetes Diagnosis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-75329-9_34

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DOI: 10.1007/978-3-031-75329-9_34

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