Jun 2026· Journal of Engineering Research and Reports· Vol 28, pp. 1-15· 0 citations
TL;DR
The findings indicate that supervised machine learning methods can support early diabetes risk classification.
Abstract
Aims: This study evaluated the effectiveness of supervised machine learning algorithms for early diabetes risk classification using demographic, behavioural, cardiovascular, and general health-related indicators. It also examined the variables most strongly associated with diabetes occurrence patterns.
Study Design: A quantitative experimental design was used, based on supervised multiclass classification and comparative machine learning evaluation.
Place and Duration of Study: The experimental analysis was conducted using the Diabetes Health Indicators BRFSS2015 dataset between March 2026 and mid-May 2026.
Methodology: The dataset contained demographic, lifestyle, cardiovascular, and general health-related variables associated with diabetes conditions. Before model implementation, duplicate inspection, exploratory data analysis, feature standardisation, and variable consistency evaluation were performed to improve analytical stability. Diabetes status was used as the target variable in a multiclass classification framework. Logistic Regression, K-Nearest Neighbours, Naïve Bayes, and AdaBoost classifiers were implemented using Python-based machine learning libraries. The dataset was divided into training and testing subsets using an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Feature importance analysis and ROC curve comparison were also performed to assess classification behaviour and variable contribution patterns.
Results: Logistic Regression achieved the highest ROC-AUC value of 0.814 and demonstrated stable discrimination across diabetes categories. AdaBoost achieved the highest accuracy score of 0.847 and produced competitive precision, recall, and F1-score values. K-Nearest Neighbours showed moderate classification capability, whereas Naïve Bayes demonstrated comparatively weaker classification consistency. Feature importance analysis identified HighBP, GenHlth, Age, BMI, CholCheck, and HighChol as influential variables.
Conclusion: The findings indicate that supervised machine learning methods can support early diabetes risk classification. Cardiovascular conditions, obesity-related indicators, and general health variables were important contributors to classification behaviour within the implemented framework.
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
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
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.
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