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Updated: Jan 14, 2026

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Machine learning-driven Diabetes Health Tracer (DHT): Optimizing prognosis using RaSK_GraDe and RaSK_GraDeL models.

Muhammad Noman1, Maria Hanif1, Abdul Hameed2

  • 1Department of Software Engineering and Artificial Intelligence, Iqra University, H-9, Islamabad, Pakistan.

Plos One
|October 21, 2025
PubMed
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This summary is machine-generated.

Machine learning models show high accuracy in predicting diabetes mellitus, offering improved healthcare management. Ensemble methods like Voting Classifier and Stacking Model achieved over 98% accuracy on the Diabetes Health Tracer dataset.

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Health Data Science

Background:

  • Diabetes mellitus is a major global health concern, with significant impact in South Asia.
  • Traditional diabetes prediction methods have limitations in reliability and efficiency.
  • Machine learning (ML) offers advanced capabilities for accurate disease prediction.

Purpose of the Study:

  • To comparatively analyze various ML algorithms for diabetes prediction.
  • To evaluate the performance of ensemble methods, including Voting Classifier (RaSK_GraDe) and Stacking Model (RaSK_GraDeL).
  • To assess the effectiveness of ML on diverse datasets, including the proposed Diabetes Health Tracer (DHT) dataset.

Main Methods:

  • Comparative analysis of ML algorithms: Random Forest, Decision Tree, SVM, KNN, Gradient Boosting.

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  • Application of ensemble techniques: Voting Classifier (RaSK_GraDe) and Stacking Model (RaSK_GraDeL).
  • Data pre-processing: handling missing values, outliers, normalization, and class balancing (SMOTE).
  • Hyperparameter tuning using cross-validation and Random Search.

    • Main Results:

    • Ensemble methods achieved high predictive accuracy: RaSK_GraDe (98.03%) and RaSK_GraDeL (98.55%) on the DHT dataset.
    • Pre-processing and hyperparameter tuning enhanced model robustness and performance.
    • ML algorithms demonstrated superior performance compared to traditional methods.

    Conclusions:

    • Machine learning techniques are highly effective for diabetes mellitus prediction.
    • Ensemble methods, particularly stacking, show significant promise for improving diagnostic accuracy.
    • The findings support the advancement of personalized treatment and healthcare management for diabetes.