A HYBRID MACHINE LEARNING MODEL FOR IMPROVED ACCURACY IN DIABETES PREDICTION USING CLINICAL DATA
Main Article Content
Abstract
The increasing prevalence of diabetes has created a critical need for accurate and efficient early prediction systems to support timely diagnosis and treatment. Machine learning techniques have shown significant potential in healthcare analytics; however, individual models often suffer from limitations in handling complex clinical data. This study proposes a hybrid machine learning model to improve the accuracy and reliability of diabetes prediction using clinical and lifestyle data. A comprehensive dataset comprising demographic and medical attributes, including age, body mass index (BMI), HbA1c level, blood glucose level, hypertension, heart disease, and smoking history, was utilized for analysis. Data preprocessing techniques such as encoding and normalization were applied to ensure consistency and enhance model performance. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors, were implemented and evaluated. A hybrid model based on a stacking ensemble approach was then developed to combine the strengths of individual classifiers. The performance of the models was assessed using evaluation metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that the proposed hybrid model outperforms individual classifiers, achieving higher predictive accuracy and improved classification performance. The model also reduces misclassification rates, making it more reliable for practical healthcare applications. The findings highlight the effectiveness of hybrid machine learning approaches in handling complex datasets and improving disease prediction outcomes. This study contributes to the development of intelligent healthcare systems by providing a robust and scalable framework for diabetes prediction. The proposed model has strong potential for integration into clinical decision support systems, enabling early diagnosis and improved patient care.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the IJRDO Journal will have the full right to remove the published article on any misconduct found in the published article.
References
2.Barik, S., Mohanty, S., Mohanty, S., & Singh, D. (2020). Analysis of prediction accuracy of diabetes using classifier and hybrid machine learning techniques. In Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 2 (pp. 399-409). Singapore: Springer Singapore.
3.Sarwar, A., Ali, M., Manhas, J., & Sharma, V. (2020). Diagnosis of diabetes type-II using hybrid machine learning based ensemble model. International Journal of Information Technology, 12(2), 419-428.
4.Rufo, D. D., Debelee, T. G., & Negera, W. G. (2022). A hybrid machine learning model based on global and local learner algorithms for diabetes mellitus prediction. Journal of Biomimetics, Biomaterials and Biomedical Engineering, 54, 65-88.
5.Anbananthen, K. S. M., Busst, M. B. M. A., Kannan, R., & Kannan, S. (2023). A comparative performance analysis of hybrid and classical machine learning method in predicting diabetes. Emerg. Sci. J, 7(1), 102-115.
6.Edeh, M. O., Khalaf, O. I., Tavera, C. A., Tayeb, S., Ghouali, S., Abdulsahib, G. M., ... & Louni, A. (2022). A classification algorithm-based hybrid diabetes prediction model. Frontiers in Public Health, 10, 829519.
7.Modak, S. K. S., & Jha, V. K. (2024). Diabetes prediction model using machine learning techniques. Multimedia Tools and Applications, 83(13), 38523-38549.
8.Jain, A., & Jain, S. (2024, November). A Novel Hybrid Model for Efficient Prediction of Diabetes Using Machine Learning. In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI) (pp. 1-6). IEEE.
9.Abnoosian, K., Farnoosh, R., & Behzadi, M. H. (2023). Prediction of diabetes disease using an ensemble of machine learning multi-classifier models. BMC bioinformatics, 24(1), 337.
10.Li, X., Zhang, J., & Safara, F. (2023). Improving the accuracy of diabetes diagnosis applications through a hybrid feature selection algorithm. Neural processing letters, 55(1), 153-169.
11.Farnoosh, R., Abnoosian, K., & Isewid, R. A. (2025). Two machine-learning hybrid models for predicting type 2 diabetes mellitus. Journal of Medical Signals & Sensors, 15(4), 11.
12.Airlangga, G. (2024). Enhancing Diabetes Prediction Accuracy through Hybrid Machine Learning Models: A Comparative Study. G-Tech: Jurnal Teknologi Terapan, 8(2), 1297-1306.
13.Chowdhury, P., Barua, P., & Uddin, M. N. (2024, September). Diabetes prediction using machine learning and hybrid deep learning ensemble technique. In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS) (pp. 1-7). IEEE.
14.Goudar, R., & Aftab, N. (2024, April). Diabetes prediction using hybrid model. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-10). IEEE.
15.Dutta, A., Hasan, M. K., Ahmad, M., Awal, M. A., Islam, M. A., Masud, M., & Meshref, H. (2022). Early prediction of diabetes using an ensemble of machine learning models. International Journal of Environmental Research and Public Health, 19(19), 12378.
16.Khalid, H., Khan, A., Zahid Khan, M., Mehmood, G., & Shuaib Qureshi, M. (2023). Machine learning hybrid model for the prediction of chronic kidney disease. Computational Intelligence and Neuroscience, 2023(1), 9266889.
17.Chandramouli, A., Hyma, V. R., Tanmayi, P. S., Santoshi, T. G., & Priyanka, B. J. E. C. (2023). Diabetes prediction using hybrid bagging classifier. Entertainment Computing, 47, 100593.
18.Abd Zaid, M. M., & Mohammed, A. A. (2024). Hybrid models in diabetes prediction: A review of techniques, performance, and potential. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(4), 298-308.
19.Maniruzzaman, M., Rahman, M. J., Ahammed, B., & Abedin, M. M. (2020). Classification and prediction of diabetes disease using machine learning paradigm. Health information science and systems, 8(1), 7.
20.Dohare, S., Pamulaparthy, L., Abdufattokhov, S., Naga Ramesh, J. V., El-Ebiary, Y. A. B., & Thenmozhi, E. (2024). Enhancing Diabetes Management: A Hybrid Adaptive Machine Learning Approach for Intelligent Patient Monitoring in e-Health Systems. International Journal of Advanced Computer Science & Applications, 15(1).
21.Mustafa, M. (2023). Diabetes prediction dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset