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October 3, 2025Theoretical and Natural ScienceOpen Access

Application and Challenges of Machine Learning in Prediction of Type 2 Diabetes: A Systematic Review

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Authors

DBDemeng BaiJCJiajing Chen

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Overview

Systematic review highlights machine learning's role in predicting type 2 diabetes, indicating ongoing data quality challenges.

Key Points

  • Machine learning enhances predictive accuracy for type 2 diabetes, revealing significant potential in early detection.
  • A variety of algorithms, including deep learning and ensemble methods, are utilized to analyze multimodal data sources.
  • Challenges such as data quality issues and model interpretability hinder the effective application of these predictive models.
  • Future developments should focus on interpretable machine learning systems that can adapt to clinical settings.

Cite This Study

Bai et al. (2025) studied this question.

synapsesocial.com/papers/68e02f40f0e39f13e7fa2ae1https://doi.org/10.54254/2753-8818/2025.dl27358
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Also Consider

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

  1. 1Modern Machine Learning Approaches for Predicting Type 2 Diabetes Mellitus2025
  2. 2Data to Diagnosis: Evaluating Machine Learning Algorithms for Predictive Healthcare in Diabetes2025
  3. 3Comparative Study of Machine Learning Techniques for Diabetes Forecasting2025
  4. 4Machine Learning classifiers for non-invasive questionnaire-based Type 2 Diabetes detection2025
  5. 5A Machine Learning Model for Predicting the Risk of Developing Diabetes - T2DM Using Real-World Data from Kilifi, Kenya2025