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Diabetes, an ever-growing chronic disease, poses significant challenges when it is not detected early and diagnosed promptly. Recent advances in medical science exploit various machine learning techniques, including ontology-based ML approaches, to develop automated systems capable of efficiently detecting diabetic patients. This paper delves into an in-depth evaluation of various machine learning algorithms, with a particular focus on model performance and the influence of feature selection methods, in the context of diabetes prediction. The study highlights the important role of these techniques in optimizing automated systems for early and accurate diabetes detection, thus contributing to more effective healthcare interventions.

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Predictive Modeling of Diabetes Risk Factors and Early Complications Using Machine Learning and Deep Learning Approaches

  • 章节

  • 发表日期：

    2025年

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  • Abdelaadim Khriss,
  • Mohammed Badaoui,
  • Wiam Boutayeb,
  • Hannah Al Ali,
  • Aissa Kerkour Elmiad

摘要

Diabetes, an ever-growing chronic disease, poses significant challenges when it is not detected early and diagnosed promptly. Recent advances in medical science exploit various machine learning techniques, including ontology-based ML approaches, to develop automated systems capable of efficiently detecting diabetic patients. This paper delves into an in-depth evaluation of various machine learning algorithms, with a particular focus on model performance and the influence of feature selection methods, in the context of diabetes prediction. The study highlights the important role of these techniques in optimizing automated systems for early and accurate diabetes detection, thus contributing to more effective healthcare interventions.