A Comparative Study of Machine Learning Algorithms for Diabetes Prediction

This study evaluates the performance of six machine learning models—Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC)—on a binary classification task. Among these, Random Forest (RF) achieved the highest accuracy (78.57%) and ROC-AUC (0.83), indicating superior overall predictive capability, albeit with a lower recall (0.56), suggesting a trade-off in detecting positive cases. Gradient Boosting (GBC) and KNN demonstrated balanced performance, with competitive F1-scores (0.69 and 0.68, respectively) and robust recall (0.73 and 0.71), making them suitable for scenarios requiring a harmony between precision and sensitivity. The Decision Tree (DT) model exhibited the highest recall (0.75), excelling in identifying true positives but at the cost of lower precision (0.62). While most models (LR, KNN, SVC, RF, GBC) maintained strong ROC-AUC scores (>0.80), SVC had the lowest accuracy (73.38%) and F1-score (0.60). The results suggest that model selection should be guided by specific priorities: RF for optimal accuracy and AUC, GBC/KNN for balanced metrics, and DT for maximizing true positive detection. These findings highlight the importance of aligning model choice with application-specific requirements in classification tasks.

Diabetes Prediction, Machine Learning, Pima Indians Dataset, Logistic Regression

1. Y. Amani, J. Akhtar and R. Jawad , "A decision support system for diabetes prediction using machine learning and deep learning techniques," in 1st International informatics and software engineering conference, 2019. [Google Scholar] [Crossref]

2. M. Hasan, M. Alam, D. Das and E. Hossain, "Diabetes prediction using ensembling of different machine learning classifiers," IEEE, vol. 8, pp. 76516-76531., 2020. [Google Scholar] [Crossref]

3. D. Magliano and . B. Edward, "IDF diabetes atlas," 2022. [Google Scholar] [Crossref]

4. I. Kavakiotis, O. Tsave and A. Salifoglou, "Machine learning and data mining methods in diabetes research," Computational and structural biotechnology journal, vol. 15, pp. 104-116, 2017. [Google Scholar] [Crossref]

5. T. M. Rahman, A. M. Shamim and H. Moontahina , "An Early Diabetes Detection Framework Utilizing Interpretable Hybrid Deep Learning Model," in 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications, 2024. [Google Scholar] [Crossref]

6. D. Sisodia and D. S. Sisodia, "Prediction of diabetes using classification algorithms," Procedia computer science, vol. 132, pp. 1578-1585, 2018. [Google Scholar] [Crossref]

7. A. R. Bindiya and K. R. Nikhil, "Diabetes Mellitus Prediction using Machine Learning Algorithms.," International Journal for Research in Applied Science & Engineering Technology, 2020. [Google Scholar] [Crossref]

8. M. A. Alam and A. H. Sohel, "Machine Learning and Artificial Intelligence in Diabetes Prediction and Management," Journal of Next-Gen Engineering Systems, 2024. [Google Scholar] [Crossref]

9. A. K. Dwivedi, "Analysis of computational intelligence techniques for diabetes mellitus prediction," Neural Computing and Applications, vol. 12, 2018. [Google Scholar] [Crossref]

• A Machine Learning Model for Predicting the Risk of Developing Diabetes - T2DM Using Real-World Data from Kilifi, Kenya
• AI-Powered Facial Recognition Attendance System Using Deep Learning and Computer Vision
• A Comprehensive Review on Brain Tumour Segmentation Using Deep Learning Approach
• A Scalable Retrieval-Augmented Generation Pipeline for Domain-Specific Knowledge Applications
• Predictive Maintenance in Semiconductor Manufacturing Using Machine Learning on Imbalanced Dataset