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December 19, 2025Applied and Computational EngineeringOpen Access

Machine Learning Models for Diabetes Prediction: Logistic Regression, SVM, Random Forest, and Neural Networks

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YTYujia Tian

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Overview

Machine learning models improved diabetes risk prediction in clinical features analysis, suggesting early warning systems may enhance detection.

Key Points

  • This research aims to develop accurate predictive models for diabetes using machine learning techniques.
  • Utilized the Pima Indian Diabetes Dataset for training models.
  • Models evaluated include logistic regression, support vector machine, random forest, and neural network.
  • Performance metrics include accuracy, precision, recall, and area under ROC curve (AUC).
  • Logistic regression achieved the highest AUC of 0.84 for small medical data.
  • Key factors influencing diabetes prediction include blood glucose, body mass index, and age.
  • Logistic regression demonstrates advantages in stability and interpretability, suitable for small studies.

Cite This Study

Yujia Tian (2025) studied this question.

synapsesocial.com/papers/69449a892f0218eca9508465https://doi.org/10.54254/2755-2721/2026.tj30648
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