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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 YangBeijing Chao-Yang Hospital

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Overview

Research demonstrates machine learning models improve diabetes risk prediction accuracy, highlighting Random Forest as a leading choice.

Key Points

  • Random Forest model achieved the highest area under the ROC curve (AUC) at 0.833, indicating superior classification capability.
  • Among five machine learning models tested, Gradient Boosting reached the highest accuracy of 75.97%, showcasing its effectiveness.
  • The evaluation involved various performance metrics like precision, recall, and F1 score for robust risk stratification.
  • Analysis utilized the Pima Indians Diabetes Dataset, emphasizing the need for effective diabetes risk prediction in public health.

Cite This Study

Boyi Yang (2025) studied this question.

synapsesocial.com/papers/68af736e7567bf4f94fed9abhttps://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. 1Machine Learning Models for Diabetes Prediction: Logistic Regression, SVM, Random Forest, and Neural Networks2025
  2. 2Diabetes Disease Prediction Using Machine Learning Classification Algorithms2025
  3. 3Enhancing Diabetes Diagnosis Through Machine Learning: A Comparative Study2025 · 6 citations
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  5. 5Data to Diagnosis: Evaluating Machine Learning Algorithms for Predictive Healthcare in Diabetes2025