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September 10, 2025ITM Web of ConferencesOpen Access

Multiple Machine Learning Models-Based Diabetes Prediction and Feature Importance Analysis

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Authors

JYJianbo Ye

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Overview

Analysis demonstrates improved diabetes prediction accuracy using machine learning models, highlighting feature importance.

Key Points

  • The random forest model achieves the highest prediction accuracy of 79.870% for diabetes.
  • Decision tree model shows the lowest prediction accuracy at 72.727%, indicating model effectiveness varies.
  • Four models, including logistic regression, are analyzed for diabetes prediction, emphasizing their feature importance.
  • Key features influencing diabetes prediction are glucose, Body Mass Index (BMI), and age, based on model results.

Cite This Study

Jianbo Ye (2025) studied this question.

synapsesocial.com/papers/68c23d3ab210217d6478d471https://doi.org/10.1051/itmconf/20257802006
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Also Consider

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