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July 10, 2025Transactions on Computer Science and Intelligent Systems Research

Diabetes Risk Prediction Model Using Machine Learning

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BYBoyi Yang

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Overview

Comparative analysis of machine learning models shows Random Forest exceeds others in predicting diabetes risk.

Key Points

  • Random Forest achieved the highest AUC of 0.833 among the models tested, indicating superior classification capability.
  • The Gradient Boosting model secured an accuracy of 75.97%, demonstrating effective risk prediction performance.
  • The assessment involved various machine learning techniques including Logistic Regression, Decision Tree, and Support Vector Machine.
  • Results highlight the potential of machine learning models to enhance diabetes risk stratification in clinical practice.

Cite This Study

Boyi Yang (2025) studied this question.

synapsesocial.com/papers/68af55ccad7bf08b1eadc25dhttps://doi.org/10.62051/nzr6tw29
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Also Consider

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

  1. 1Optimizing Diabetes Prediction with Machine Learning: Model Comparisons and Insights2024 · 7 citations
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  3. 3Machine Learning Models for Diabetes Prediction: Logistic Regression, SVM, Random Forest, and Neural Networks2025
  4. 4Diabetes Disease Prediction Using Machine Learning Classification Algorithms2025
  5. 5Optimizing Diabetes Prediction Accuracy: A Comprehensive Approach with Advanced Preprocessing and Diverse Machine Learning Classifiers2024 · 9 citations