Comparative Analysis of Machine Learning Models for Diabetes Prediction

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@article{1724619,
      author = {Jayanthi M, Amrit Rijal},
      title = {Comparative Analysis of Machine Learning Models for Diabetes Prediction},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {279-285},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724619.pdf},
      abstract = {Early detection of diabetes can reduce serious complications. This paper compares eight supervised machine learning classifiers—Logistic Regression, k-Nearest Neighbors, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, AdaBoost and Gradient Boosting—for diabetes prediction on the Pima Indians Diabetes dataset (768 records, 8 features). All models share the same preprocessing and are evaluated with a hold-out test set, 10-fold cross-validation repeated three times, hyperparameter tuning, and class weighting, using accuracy, precision, recall, F1-score and ROC-AUC. Under repeated cross-validation, Gradient Boosting had the highest accuracy (76.9%) and F1-score (0.649), while Logistic Regression had the highest ROC-AUC (0.837). The Decision Tree detected the most diabetic cases on the test set (72.2% recall). Class weighting raised the recall of Logistic Regression from 50.0% to 70.4%. Differences between the top models were within one standard deviation, and glucose was the most influential feature.},
      keywords = {diabetes prediction; machine learning; classification; model comparison; cross-validation; Pima Indians dataset},
      month = {November},
  }