Jul 2026· International Journal of Science and Research Archive· 0 citations
TL;DR
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.
Abstract
This study addresses the challenge of delayed diagnosis of diabetes, a condition that often leads to severe complications if not detected early. The primary objective is to evaluate and compare the performance of three machine learning classifiers XGBoost, Support Vector Machine (SVM), and Random Forest for early diabetes prediction using clinical and lifestyle data. The study utilizes the Diabetes Health Indicators dataset, which includes features such as body mass index (BMI), blood pressure, cholesterol levels, and physical activity. The dataset was sourced from a publicly available repository and preprocessed through handling missing values, feature scaling, and encoding categorical variables. The models were trained on the processed dataset and evaluated using accuracy, precision, recall, and F1-score metrics, alongside exploratory data analysis to understand feature relationships. Results show that all three models performed effectively, with XGBoost achieving the highest accuracy of 85.11%, followed by SVM at 84.82%, and Random Forest at 83.16%. These findings highlight the strength of ensemble and boosting techniques in handling complex health data and accurately predicting diabetes risk. In conclusion, machine learning models demonstrate strong potential for supporting early diabetes diagnosis and improving clinical decision-making. 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.
Diabetes is one of the leading causes of morbidity and mortality worldwide. To avoid difficult management of the condition, there is need to predict early onset of the condition. This study investigated the application of machine learning techniques and exploratory data analysis to forecast early-onset of diabetes using the PIMA Indian dataset. Preprocessing included handling missing values and standardization, leading to the development and evaluation of 5 models which include Logistic Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, and Neural Network. Exploratory analysis identified glucose concentration, body mass index, and age as the most influential features. Random Forest achieved the highest accuracy (0.74%) while both Random Forest and Logistic Regression attained the best ROC-AUC score of 0.81%. Feature importance analysis emphasized the predictive significance of glucose and BMI, aligning with clinical knowledge of diabetes risk factors. Despite the promising results the study acknowledged limitations related to the PIMA dataset's demographic scope and the moderate complexity of neural networks, highlighting areas for future enhancement. Ethical considerations, including data privacy and algorithmic bias, were addressed to ensure responsible model development.
Abdulrahman Nasiru Sada, Eli. A. Jiya, Yahaya Muhammad Umar et al.· Journal of Science Research...· 0 citations
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
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
Experimental results demonstrate that Machine Learning techniques can effectively predict disease occurrence with high accuracy, thereby assisting healthcare professionals in early diagnosis and treatment planning.
Sunidhi, Mothe Rahul, M. Kumar et al.· International Journal for Re...· 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
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
