Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights

Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights

Diabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals....

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Bibliographic Details
Main Authors: Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Information
Subjects:
AutoML
explainable AI (XAI)
diabetes prediction
SHAP
LIME
counterfactual analysis
Online Access:https://www.mdpi.com/2078-2489/16/1/7
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Summary:Diabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. Using the Pima Indian Diabetes dataset, we developed an ensemble model with 85.01% accuracy leveraging AutoGluon’s AutoML framework. To address the “black-box” nature of machine learning, we applied XAI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Integrated Gradients (IG), Attention Mechanism (AM), and Counterfactual Analysis (CA), providing both global and patient-specific insights into critical risk factors such as glucose and BMI. These methods enable transparent and actionable predictions, supporting clinical decision-making. An interactive Streamlit application was developed to allow clinicians to explore feature importance and test hypothetical scenarios. Cross-validation confirmed the model’s robust performance across diverse datasets. This study demonstrates the integration of AutoML with XAI as a pathway to achieving accurate, interpretable models that foster transparency and trust while supporting actionable clinical decisions.
ISSN:2078-2489

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