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<p>This comprehensive survey explores diverse diabetes prediction models intending to enhance pattern comprehension and enable early diagnosis. The implications of this research extend to improved insulin treatment and reduced risks of associated complications. Notably, our investigation applies infrequently utilized machine learning (ML) models to diabetes datasets, yielding accuracy rates ranging from 65% to 97%. The recommendation emerges to leverage various algorithms for insulin diagnosis, with a particular emphasis on creating hybrid algorithms to enhance overall performance. A health classification technique rooted in ML is proposed for categorizing individuals into diabetic and non-diabetic groups. Our research spans multiple resources, contributing valuable insights for future studies on diverse diabetes prediction methods. This paper outlines the current landscape of ML algorithms for diabetic prediction, emphasizing the significance of employing varied algorithms. The study delves into an array of prediction techniques, focusing on the widely used Pima dataset. Among the 15 ML algorithms explored, methodologies such as Support Vector Machine (SVM) and Naive Bayes (NBs) are applied. The strategic use of these techniques conserves resources and yields more precise findings, contributing to anticipatory measures against diabetes. Readers are urged to explore this research, as it encapsulates the forefront of diabetic prediction through ML algorithms. The amalgamation of insights from diverse studies facilitates a nuanced understanding of the subject. This survey encapsulates key findings, emphasizing the importance of employing a spectrum of ML algorithms for optimal predictive outcomes.</p>

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A Survey of Machine Learning Techniques for Diabetes Prediction: Current Trends and Future Directions

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Mudra Narasimharao,
  • Biswaranjan Swain,
  • Rahul Priyadarshi,
  • Praveen Priyaranjan Nayak,
  • Satyanarayan Bhuyan

摘要

This comprehensive survey explores diverse diabetes prediction models intending to enhance pattern comprehension and enable early diagnosis. The implications of this research extend to improved insulin treatment and reduced risks of associated complications. Notably, our investigation applies infrequently utilized machine learning (ML) models to diabetes datasets, yielding accuracy rates ranging from 65% to 97%. The recommendation emerges to leverage various algorithms for insulin diagnosis, with a particular emphasis on creating hybrid algorithms to enhance overall performance. A health classification technique rooted in ML is proposed for categorizing individuals into diabetic and non-diabetic groups. Our research spans multiple resources, contributing valuable insights for future studies on diverse diabetes prediction methods. This paper outlines the current landscape of ML algorithms for diabetic prediction, emphasizing the significance of employing varied algorithms. The study delves into an array of prediction techniques, focusing on the widely used Pima dataset. Among the 15 ML algorithms explored, methodologies such as Support Vector Machine (SVM) and Naive Bayes (NBs) are applied. The strategic use of these techniques conserves resources and yields more precise findings, contributing to anticipatory measures against diabetes. Readers are urged to explore this research, as it encapsulates the forefront of diabetic prediction through ML algorithms. The amalgamation of insights from diverse studies facilitates a nuanced understanding of the subject. This survey encapsulates key findings, emphasizing the importance of employing a spectrum of ML algorithms for optimal predictive outcomes.