THE MACHINE LEARNING-BASED DIABETES PREDICTION TOOL

Öz

Machine learning methods are widely used in many areas, including engineering, healthcare, manufacturing, finance, transportation, and marketing because of their diversity, applicability, and high performances. Since diabetes mellitus is considered one of the most important health problems of our time, early diagnosis is extremely important to protect public health and reduce various complications caused by the disease. In this study, a new desktop-based clinical decision support software tool was developed to predict diabetes using numerous different machine learning algorithms. This feature-rich prediction tool aims to enable decision-makers to detect diabetes easily, quickly, and effectively with many machine learning models by generating individual risk probabilities. 

Anahtar Kelimeler

• Machine learning
• Deep learning
• Diabetes
• Prediction tool

Makine Öğrenimi Tabanlı Diyabet Tahmin Aracı

Öz

Makine öğrenimi yöntemleri; çeşitlilikleri, uygulanabilirlikleri ve yüksek performansları nedeniyle mühendislik, sağlık, üretim, finans, ulaşım ve pazarlama gibi birçok alanda yaygın olarak kullanılmaktadırlar. Diyabet, günümüzün en önemli sağlık sorunlarından biri olarak kabul edildiğinden, halk sağlığını korumak ve hastalığın neden olduğu çeşitli komplikasyonları azaltmak için erken teşhis son derece önemlidir. Bu çalışmada, çok sayıda farklı makine öğrenimi algoritması kullanarak diyabet tahmini gerçekleştiren yeni bir masaüstü tabanlı klinik karar destek yazılım aracı geliştirilmiştir. Birçok özelliğe sahip bu tahmin aracı, bireysel risk olasılıkları üreterek karar vericilerin birçok makine öğrenimi modeli kullanarak diyabeti kolayca, hızlı ve etkili bir şekilde tespit etmelerini sağlamayı amaçlamaktadır.

Anahtar Kelimeler

• Makine öğrenimi
• Derin öğrenme
• Diyabet
• Tahmin aracı

Kaynakça

1. Alpaydin, E. (2010) Introduction to Machine Learning, 2nd ed., MIT Press, England.
2. Alzboon, M.S., Al-Batah, M.S., Alqaraleh, M., Abuashour, A., Bader, A.F.H. (2023) Early diagnosis of diabetes: A comparison of machine learning methods, International Journal of Online and Biomedical Engineering, 19(15), 144-165. doi: 10.3991/ijoe.v19i15.42417
3. Boser, B.E., Guyon, I.M., Vapnik, V.N. (1992) A training algorithm for optimal margin classifiers, Proceedings of 5th annual workshop on Computational learning theory COLT’92, Pittsburgh, PA, 144-152. doi: 10.1145/130385.130401
4. Breiman, L. (2001) Random forests, Machine Learning, 45, 5-32. doi: 10.1023/A:1010933404324
5. Dhawan, L., Bansal, A. (2025) Beyond Machine Learning: Exploring Deep Learning and Transformers for Software Defect Prediction. In: Swaroop, A., Virdee, B., Correia, S.D., Polkowski, Z. (eds) Proceedings of Data Analytics and Management. ICDAM 2024, Lecture Notes in Networks and Systems, vol 1301, Springer, Singapore. doi: 10.1007/978-981-96-3372-2_7
6. Feng, X., Cai, Y., Xin, R. (2023) Optimizing diabetes classification with a machine learning-based framework, BMC Bioinformatics, 24, 428. doi: 10.1186/s12859-023-05467-x
7. García-Jaramillo, M., Luque, C., León-Vargas, F. (2024) Machine learning and deep learning techniques applied to diabetes research: A bibliometric analysis, Journal of Diabetes Science and Technology, 18(2), 287-301. doi: 10.1177/1932296823121
8. Ho, T.K. (1995) Random decision forests, Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 278-282. doi: 10.1109/ICDAR.1995.598994

9. Hochreiter, S., Schmidhuber, J. (1997) Long short-term memory, Neural Computation, 9(8), 1735-1780. doi: 10.1162/neco.1997.9.8.1735
10. Hosmer, D.W., Lemeshow, S., Sturdivant, R.X. (2013) Applied Logistic Regression, 3rd ed., Wiley.
11. https://customtkinter.tomschimansky.com/, Accessing date: Accessing date: 2025, CustomTkinter.
12. https://keras.io/, Accessing date: 2025, Keras.
13. https://matplotlib.org/, Accessing date: 2025, Matplotlib.
14. https://numpy.org/, Accessing date: 2025, NumPy.
15. https://pypi.org/project/pillow/, Accessing date: 2025, Pillow.
16. https://pytorch.org/, Accessing date: 2025, PyTorch.
17. https://scikit-learn.org/stable/, Accessing date: 2025, scikit-learn.
18. https://scipy.org/, Accessing date: 2025, SciPy.
19. https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database, Accessing date: 2025, Pima Indians Diabetes Database.
20. https://www.python.org/, Accessing date: 2025, Python 3.12.0.
21. https://www.tensorflow.org/, Accessing date: 2025, TensorFlow.
22. International Diabetes Federation (2025) IDF Diabetes Atlas, 11th edn. Brussels, Belgium. Available at: https://diabetesatlas.org
23. Joshi, A.V. (2020) Machine Learning and Artificial Intelligence, Springer.
24. Khan, F.A., Zeb, K., Al-Rakhami, M., Derhab, A., Bukhari, S.A.C. (2021) Detection and prediction of diabetes using data mining: A comprehensive review, IEEE Access, 9, 43711-43735. doi: 10.1109/ACCESS.2021.3059343
25. Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J. (2022) A survey of Convolutional Neural Networks: Analysis, applications, and prospects, IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999-7019. doi: 10.1109/TNNLS.2021.3084827
26. Mienye, I.D., Sun, Y. (2022) A Survey of ensemble learning: Concepts, algorithms, applications, and prospects, IEEE Access, 10, 99129-99149. doi: 10.1109/ACCESS.2022.3207287
27. Modak, S.K.S., Jha, V.K. (2024) Diabetes prediction model using machine learning techniques, Multimedia Tools and Applications, 83, 38523-38549. doi: 10.1007/s11042-023-16745-4
28. Nayak, P., Jyothi, J.S.N., Harika, V., Swaraja, K., Sai Hanuman, A. (2025) Diabetes monitoring and prediction using computational intelligence techniques: A systematic review, SN Computer Science, 6, 267.doi: 10.1007/s42979-025-03802-y
29. Peterson, L.E. (2009) K-nearest neighbor, Scholarpedia, 4(2): 1883. doi: 10.4249/scholarpedia.1883
30. Sharma, T., Shah, M. (2021) A comprehensive review of machine learning techniques on diabetes detection, Visual Computing for Industry, Biomedicine and Art, 4, 30. doi: 10.1186/s42492-021-00097-7
31. Sun, Q., Cheng, X., Han, K., Sun, Y., Ren, H., Li, P. (2025) Machine learning-based assessment of diabetes risk, Applied Intelligence, 55, 106. doi: 10.1007/s10489-024-05912-1
32. Van Houdt, G., Mosquera, C., Nápoles, G. (2020) A review on the long short-term memory model, Artificial Intelligence Review, 53, 5929–5955. doi: 10.1007/s10462-020-09838-1
33. Wee, B.F., Sivakumar, S., Lim, K.H., Wong, W.K., Juwono, F.H. (2024) Diabetes detection based on machine learning and deep learning approaches, Multimedia Tools and Applications, 83, 24153-24185. doi: 10.1007/s11042-023-16407-5
34. Wickramasinghe, I., Kalutarage, H. (2021) Naïve Bayes: Applications, variations and vulnerabilities: a review of literature with code snippets for implementation, Soft Computing, 25, 2277-2293. doi: 10.1007/s00500-020-05297-6
35. Wlodarczak, P. (2020) Machine Learning and its Applications, CRC Press.