XGBoost was the most effective algorithm for identifying individuals with T2DM and provided insights into the relative importance of clinical features, supporting more precise classification, however, results should be interpreted with caution until validated in independent cohorts.
Abstract
Objectives Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder associated with serious complications, including nephropathy, cardiovascular disease, retinopathy, and neuropathy. Given its increasing incidence and the complexity of associated factors—such as obesity, metabolic syndrome, and sedentary lifestyle—accurate identification is essential. This study aimed to evaluate and compare the performance of several machine learning algorithms to identify key associated factors and detect individuals with T2DM within this dataset. Methods A publicly available dataset from Kaggle, comprising health records of 99,982 individuals, was used. Five supervised machine learning models were evaluated: Bayesian ridge regression, logistic regression, extreme gradient boosting (XGBoost), artificial neural networks, and random forest. Each model was trained and evaluated to assess classification performance. Performance was measured using the area under the receiver operating characteristic curve (AUC–ROC) and accuracy. SHapley Additive Explanations (SHAP) values were used to interpret model outputs and identify the most influential features. Results Among the five models, XGBoost demonstrated the highest performance, achieving an accuracy of 96% and an AUC–ROC of 0.98. SHAP analysis identified hemoglobin A1c, blood glucose, age, body mass index, and sex as the most influential predictors of T2DM. Conclusion: XGBoost was the most effective algorithm for identifying individuals with T2DM in this dataset. It also provided insights into the relative importance of clinical features, supporting more precise classification. However, results should be interpreted with caution until validated in independent cohorts.
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
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
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.
Ayeni Taiwo Michael, Odukoya Ayooluwa, Ilesanmi Opeyemi· Asian Research Journal of Ma...· 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 is a chronic metabolic disease characterized by high blood glucose levels that can lead to severe complications affecting various organs. The increasing prevalence of diabetes requires an accurate and efficient diagnostic approach to support early detection and medical decision-making. The development of machine learning technology provides opportunities to assist disease diagnosis by analyzing patient health data and identifying patterns associated with diabetes conditions. This study aims to compare the accuracy performance of the Naive Bayes and CatBoost algorithms in diagnosing diabetes using the confusion matrix evaluation method. The research method consists of several stages, including data collection, data preprocessing, model selection, model training, and model evaluation. The dataset used was obtained from Kaggle, consisting of 768 records with nine attributes, including pregnancies, glucose level, blood pressure, skin thickness, insulin, body mass index, diabetes pedigree function, age, and outcome classification. The models were trained using 80% training data and 20% testing data. The evaluation results showed that the Naive Bayes algorithm achieved an accuracy of 79%, while the CatBoost algorithm obtained a higher accuracy of 81%. These findings indicate that CatBoost provides better classification performance compared to Naive Bayes for diabetes diagnosis in this dataset. Therefore, CatBoost can be considered a more effective machine learning approach to support diabetes prediction. Future research is recommended to utilize larger datasets, optimize model parameters, and explore other advanced algorithms to improve diagnostic accuracy.
Basyach Aryodito Tranggono Heryanto, Onny Marleen· Journal of social research· 0 citations
