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The central aim of this research encompassed the development and implementation of a machine learning based method for predicting diabetes, alongside an exploration of effective strategies to ensure its success. Diabetes ranks among the most severe global health conditions, resulting from a confluence of variables, including high blood sugar, obesity, and other causes. It causes improper metabolism and high blood sugar levels by interfering with the activity of the insulin hormone. The main objective of this program is to predict possible cases in order to reduce the risk of diabetes and encouraging individuals to adopt healthier dietary and lifestyle choices in the future by creating and implementing a machine learning based diabetes prediction using a diverse range of algorithms, including KNN,SVC, DT, RF, and GBC.

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DiabeXpert: A Comparative Machine Learning Framework for Diabetes Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Jacinth Peyyala Satvik,
  • Chidurala Sanjana,
  • Budigem Dhuhitha,
  • Lingam Sunitha

摘要

The central aim of this research encompassed the development and implementation of a machine learning based method for predicting diabetes, alongside an exploration of effective strategies to ensure its success. Diabetes ranks among the most severe global health conditions, resulting from a confluence of variables, including high blood sugar, obesity, and other causes. It causes improper metabolism and high blood sugar levels by interfering with the activity of the insulin hormone. The main objective of this program is to predict possible cases in order to reduce the risk of diabetes and encouraging individuals to adopt healthier dietary and lifestyle choices in the future by creating and implementing a machine learning based diabetes prediction using a diverse range of algorithms, including KNN,SVC, DT, RF, and GBC.