Diabetes prediction models based on intrinsic explainable machine learning

Xiangtong Huang; Jing Zhang; Qi Chen; Jia He

doi:10.1117/12.3051788

18 November 2024 Diabetes prediction models based on intrinsic explainable machine learning

Xiangtong Huang, Jing Zhang, Qi Chen, Jia He

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032P (2024) https://doi.org/10.1117/12.3051788
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

With the sharp increase in the global prevalence of diabetes, early diagnosis and prediction have become key to improving patient management and reducing the disease burden. This study investigates the use of explainable machine learning models for diabetes prediction, specifically employing the Pima Indian Diabetes dataset to develop a model that integrates explainability. Several advanced machine learning models—including the Bayesian classifier, Decision Tree, XGBoost, LightGBM, Random Forest, K-Nearest Neighbors (KNN), and CatBoost—were assessed using the 2019 Diabetics dataset. Notably, the SHAP framework was applied to the Pima Indian dataset to enhance the transparency and interpretability of the models. Through a comprehensive evaluation of different models, we found that the CatBoost model performed excellently across multiple performance metrics, particularly achieving an AUC value of 0.867, indicating superior sensitivity and specificity compared to other models. Additionally, the application of the SHAP framework revealed that glucose levels and BMI are the main factors influencing diabetes prediction, providing valuable insights for medical professionals to better understand and explain the prediction results. This study confirms the effectiveness of explainable machine learning methods in improving the accuracy of early diabetes prediction and model transparency. Our results offer new perspectives and approaches for applying machine learning technologies to diabetes and other chronic diseases in clinical practice, promoting the development of personalized and precision medicine.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Xiangtong Huang, Jing Zhang, Qi Chen, and Jia He "Diabetes prediction models based on intrinsic explainable machine learning", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032P (18 November 2024); https://doi.org/10.1117/12.3051788

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KEYWORDS

Machine learning

Data modeling

Performance modeling

Random forests

Decision trees

Transparency

Biomedical applications

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