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September 10, 2025Indonesian Journal of Computer ScienceOpen Access

Enhancing Diabetes Prediction Accuracy Using Stacked Machine Learning and Deep Learning Models: A Public Health Approach

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Authors

MIMonirul Islam

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Overview

Analysis reveals enhanced diabetes prediction accuracy using ensemble methods, indicating a need for ethical approaches.

Key Points

  • The weighted soft voting ensemble improved prediction accuracy for diabetes classification, achieving 75.65% accuracy.
  • Precision reached 67.89% with ROC-AUC of 81.41%, demonstrating strong performance over individual models.
  • Using the Indian PIMA Diabetes dataset, the method combined machine learning and deep learning techniques.
  • Ethical considerations are vital for the implementation of AI in diabetes care, emphasizing the need for responsible practices.

Cite This Study

Monirul Islam (2025) studied this question.

synapsesocial.com/papers/68c1a5ff54b1d3bfb60dff86https://doi.org/10.33022/ijcs.v14i4.4947
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Improved Stacked Ensemble Technique in Enhancing the Classification of Diabetes Mellitus Patients2026
  2. 2Advanced Ensemble Machine Learning Techniques for Optimizing Diabetes Mellitus Prognostication: A Detailed Examination of Hospital Data2024 · 29 citations
  3. 3Comparative Performance Analysis of Selected Machine Learning Algorithms and the Stacking Ensemble Method for Prediction of the Type II Diabetes Disease2024 · 1 citations
  4. 4A Sample-Level Entropy-Weighted Stacking Method for Diabetes Prediction2025
  5. 5Diabetes Prediction Using Machine Learning2024 · 5 citations