Jul 2026· Asian Research Journal of Mathematics· 0 citations
TL;DR
Random Forest provided the best overall performance for three-class diabetes classification in this analytical sample, however, modest agreement, low prediabetes sensitivity, potential label leakage from fasting glucose, and the absence of external validation indicate that further evaluation is required before clinical application.
Abstract
Background: Early detection of diabetes and prediabetes is important for reducing long-term complications.
Aim: This study comparatively evaluated four machine learning algorithms for three-class diabetes classification using routinely available clinical indicators from the National Health and Nutrition Examination Survey.
Methods: The analytical sample included 2,029 participants classified as normal (62.0%), prediabetic (26.7%), or diabetic (11.3%). Recursive Feature Elimination with 10-fold cross-validation was used to select predictors from 27 candidate variables. Six features were retained: fasting glucose, age, diabetes history, insulin level, waist circumference, and systolic blood pressure. Multinomial Logistic Regression, Decision Trees, Random Forest, and XGBoost were trained using a 70/30 stratified train-test split and evaluated using accuracy, Cohen’s Kappa, and class-specific performance metrics.
Results: Random Forest achieved the highest overall test performance, with 78.1% accuracy and a Cohen’s Kappa of 0.471. XGBoost, Multinomial Logistic Regression, and Decision Trees achieved accuracies of 72.6%, 73.0%, and 72.4%, respectively. All models showed high specificity for diabetes detection, exceeding 97%. Prediabetes classification remained difficult across algorithms, with sensitivity ranging from 38% to 42%.
Conclusion: Random Forest provided the best overall performance for three-class diabetes classification in this analytical sample. However, modest agreement, low prediabetes sensitivity, potential label leakage from fasting glucose, and the absence of external validation indicate that further evaluation is required before clinical application.
Diabetes mellitus is a chronic metabolic disorder whose global prevalence continues to rise, creating an urgent need for scalable, low-cost tools for early risk identification. This study evaluates the effectiveness of five machine learning algorithms (Random Forest, XGBoost, Support Vector Machine [SVM], CatBoost, and TabNet) for predicting diabetes risk from routinely available clinical and lifestyle variables. Using the Pima Indians Diabetes dataset, a preprocessing pipeline was applied that included median imputation of physiologically implausible zero values, standardized (Z-score) feature scaling, Boruta-based feature selection, and class-imbalance handling through class-weight adjustment and the Synthetic Minority Oversampling Technique (SMOTE). Models were trained on an 80/20 train-test split and assessed using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC-AUC). Random Forest achieved the strongest overall performance (accuracy 0.753; F1-score 0.689; ROC-AUC 0.810), followed by CatBoost, SVM, and XGBoost, whereas TabNet performed worst with very low recall for the diabetic class. The best-performing model (Random Forest) was deployed in a lightweight Flask web application that returns a probability-based diabetes risk assessment, categorising each prediction as low, moderate, or high risk together with a tailored recommendation. The findings confirm that ensemble tree-based methods, particularly Random Forest, provide a reliable, interpretable, and deployable basis for diabetes risk screening, especially in resource-constrained settings. Key limitations include dataset homogeneity, residual class imbalance, and limited feature coverage.
T. Olayinka· FUDMA Journal of Sciences· 0 citations
It is recommended that healthcare systems adopt XGBoost-based predictive models in clinical decision support tools for early screening, while future studies should validate these models using real-world clinical data to enhance reliability and generalizability.
Idehen Emmanuel Imafidon, Chikere Obinna Munachiso, Dominic Evans Onyebuchi et al.· International Journal of Sci...· 0 citations
Findings suggest that Random Forest and Logistic Regression are the most promising for assisting in preliminary diabetes prediction, although further validation on diversity datasets is needed prior to clinical deployment.
F. Setiawan, Tri Sugihartono· SinkrOn· 0 citations
Diabetes affects over 101 million people in India, with many more at risk due to routine and hereditary factors. Early diagnosis is crucial to prevent complications, which make accurate predictive tools essential in healthcare. This research uses Machine Learning (ML) algorithms to evaluate the likelihood of Type 2 Diabetes Mellitus (T2DM) using lifestyle and family history data. The trained models demonstrate strong predictive ability, allowing individuals to self-assess their risk and supporting healthcare professionals in early detection and intervention. This study presents a performance assessment of seven ML classifiers: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (k-NN), and Extreme Gradient Boosting (XGBoost). These classifiers were applied to the widely used PIMA Indian Diabetes dataset (PIDD), which contains 768 clinical records of adult women aged 21 and above, providing key medical information for diabetes analysis. Multiple evaluation measures were applied to assess model performance with results showing that SVM achieved the highest accuracy and AUC, while LR, RF, and XGBoost also performed competitively. Although k-NN attained the highest recall, it yielded a higher false positive rate. These findings highlight that no single model is perfect for every situation, and the choice of classifier should match clinical needs. This study serves as a reference for ML applications in diabetes prediction.
Rizwan Akhtar, Muhammad Kalamuddin Ahamad· ITEGAM- Journal of Engineeri...· 0 citations
Objective This study aimed to develop a machine learning (ML) framework to predict incident type 2 diabetes mellitus (T2DM) using routinely available hematological and renal biomarkers, and to assess their added predictive value over conventional clinical risk factors. Methods We analyzed data from 6093 diabetes-free participants from the prospective Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort. Predictors included white blood cell count (WBC), red blood cell count (RBC), red cell distribution width (RDW), and other hematological/renal factors. We employed logistic regression and multiple ML models (Random Forest, XGBoost, LightGBM), optimized via grid search and cross-validation. Results Multivariate logistic regression identified RBC, WBC, and RDW as independent predictors of T2DM. The Random Forest model achieved the highest performance with a ROC-AUC of 0.73, an accuracy of 0.67, and correctly identified 223 of 347 incident T2DM cases Using a probability threshold of 0.20, the Random Forest model achieved a sensitivity of 0.618, a specificity of 0.707, a positive predictive value (PPV) of 0.327, and a negative predictive value (NPV) of 0.889 on the independent test set Feature importance analysis identified metabolic syndrome, BMI, uric acid, and age as the strongest contributors, while WBC, RBC, and NLR were the most influential hematological predictors. Conclusion Routine hematological indices, including RBC, WBC, and RDW, were independently associated with incident T2DM, while metabolic syndrome, BMI, uric acid, and age contributed most strongly to overall model prediction. ML provides a complementary approach for early risk stratification, although further validation is required before clinical implementation.
Niloufar Kamkar, Saleh Behzadi, Vahid Mahdavizadeh et al.· American Heart Journal Plus:...· 0 citations
Six supervised learning models were developed and compared for diabetes prediction using a dataset and compared for diabetes prediction using a 100k patients records with eight clinical features including gender, age, hypertension, smoking history, heart disease, BMI, HbA1c level, and blood glucose level.