Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence

• URL: http://arxiv.org/abs/2501.18071v2
• Date: Wed, 12 Feb 2025 11:31:48 GMT
• Title: Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence
• Authors: Pir Bakhsh Khokhar, Viviana Pentangelo, Fabio Palomba, Carmine Gravino,
• Abstract summary: This study presents a framework for diabetes prediction using Machine Learning (ML) models and XAI tools. The ensemble model provided high accuracy, with a test accuracy of 92.50% and an ROC-AUC of 0.975. The results suggest that ML combined with XAI is a promising means of developing accurate and computationally transparent tools for use in healthcare systems.
• Score: 8.224338294959699
• License:
• Abstract: Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a framework for diabetes prediction using Machine Learning (ML) models, complemented with eXplainable Artificial Intelligence (XAI) tools, to investigate both the predictive accuracy and interpretability of the predictions from ML models. Data Preprocessing is based on the Synthetic Minority Oversampling Technique (SMOTE) and feature scaling used on the Diabetes Binary Health Indicators dataset to deal with class imbalance and variability of clinical features. The ensemble model provided high accuracy, with a test accuracy of 92.50% and an ROC-AUC of 0.975. BMI, Age, General Health, Income, and Physical Activity were the most influential predictors obtained from the model explanations. The results of this study suggest that ML combined with XAI is a promising means of developing accurate and computationally transparent tools for use in healthcare systems.

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