An optimized diabetes mellitus detection model for improved prediction of accuracy and clinical decision-making

Abstract

Diabetes Mellitus (DM) is an enduring metabolic illness that disturbs many individuals globally. This study addresses the global impact of Diabetes Mellitus (DM) and emphasizes the critical role of accurate DM detection in early diagnosis, effective treatment, and prevention of complications. The research introduces an optimized DM detection model, the GBM-DRU (Gradient Boosting Machine - Data Reduction Unit) network, which integrates feature engineering and ensemble learning techniques to enhance prediction accuracy and support clinical decision-making. The GBM-DRU network combines the powerful gradient boosting machine algorithm with a data reduction unit (DRU) for efficient feature selection, reducing dimensionality and improving computational efficiency. Feature engineering enhances discriminatory power, while ensemble learning methods, including bagging and boosting, improve overall model performance. Rigorous experiments on a comprehensive dataset of DM patients demonstrate that the proposed approach outperforms existing models in terms of accuracy, sensitivity, specificity, and AUC-ROC. The optimized model provides valuable insights into feature importance, aiding clinical decision-making and deepening the understanding of DM risk factors. Therefore, the GBM-DRU network, utilizing feature engineering and ensemble learning, presents a viable approach to precise diagnosis of diabetes mellitus, with favorable implications for patient outcomes, disease control, and public health campaigns. The improved prediction accuracy, feature interpretability, and clinical decision support capabilities of the model may have a beneficial effect on public health campaigns, disease management, and patient outcomes.

Keywords

• Diabetes mellitus
• GBM-DRU network
• Feature engineering
• Ensemble learning
• Clinical decision-making