Performance comparison machine learning algorithms in diabetes disease prediction

Year 2023, , 178 - 183, 20.09.2023

Aslı Göde , Adnan Kalkan

https://doi.org/10.26701/ems.1335503

Cited By: 2

Abstract

Machine learning has been widely used in the field of medicine with the developing technology in recent years. Machine learning is a field that is also used in the diagnosis of diabetes and helps experts make decisions. Diabetes is a lifelong disease that is common worldwide and in our country. The main purpose of this study is to diagnose diabetes early using different machine learning classification algorithms. Another purpose of the study is to compare the success of the machine learning models used.
Early diagnosis of diabetes allows to lead a healthy and normal life. In this context, it has been tried to diagnose diabetes early by using the machine learning techniques Decision Tree, Random Forests, K-Nearest Neighbor and Support Vector Machines classifiers on the Pima Indians Diabetes dataset. The dataset includes 9 features and 768 samples. Success evaluation of classifiers was made using Accuracy, Precision, Recall, F1-Score and AUC metrics. Random Forests gave the best results with 80 percent accuracy. This paper is to examine the association of different machine learning techniques usage, diabetes data diagnostic capabilities, diagnosis of diabetes in women diabetes patients and comparison of performances for machine learning techniques. Implications for theory and practice have been discussed. In this study, comparisons were made using different algorithms from the classification algorithms used in the literature and contributed to the literature in this field.

Keywords

Diabetes Disease, Machine Learning, Classification, Random Forests

References

• Çoşansu, G. (2015). Diyabet: küresel bir salgın hastalık. Okmeydanı Tıp Dergisi, 31:, 1-6. doi:10.5222/otd.2015.001. Türkiye Diyabet Vakfı, (accessed date: 01 January 2023). https://www.turkdiab.org/diyabet-hakkinda-hersey.asp?lang=TR&id=59
• Pulat, M., Kocakoç, I., D. (2021). Bibliometric analysis of published theses in the field of machine learning and decision trees in Turkey. Journal of Management and Economics, 28(2): 287-308. doi: 10.37990/medr.1077024.
• Bi, Q., Goodman, K., E., Kaminsky, J., Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12): 2222-2239. doi: 10.1093/aje/kwz189.
• Peng, G., C., Alber, M., Buganza Tepole, A., Cannon, W., E., De, S., Dura-Bernal, S., Kuhl, E. (2021). Multiscale modeling meets machine learning: What can we learn?. Archives of Computational Methods in Engineering, 28(3):1017-1037. doi:10.1007/s11831-020-09405-5.
• Benos, L., Tagarakis, A., C., Dolias, G., Berruto, R., Kateris, D., Bochtis, D. (2019). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11): 3758. doi: 10.3390/s21113758.
• Humelnicu, C., Ciortan, S., Amortila, V. (2019). Artificial neural network-based analysis of the tribological behavior of vegetable oil–diesel fuel mixtures. Lubricants, 7(4): 32. doi: 10.3390/lubricants7040032.
• Ray, S. (2019). A quick review of machine learning algorithms. COMITCon 2019 Conference Proceedings, p. 35-39. doi: 10.1109/COMITCon.2019.8862451.
• Faruque, M., F., Sarker, I., H. (2019). Performance analysis of machine learning techniques to predict diabetes mellitus. ECCE 2019 Conference Proceedings, p. 1-4. doi: 10.1109/ECACE.2019.8679365.
• Haq, A., U., Li, J., P., Khan, J., Memon, M., H., Nazir, S., Ahmad, S., Ali, A. (2020). Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data. Sensors, 20(9): 2649. doi: /10.3390/s20092649.
• Dritsas, E., Trigka, M. (2022). Data-driven machine-learning methods for diabetes risk prediction. Sensors, 22(14): 5304. doi: 10.3390/s22145304.
• Khanam, J., J., Foo, S., Y. (2021). A comparison of machine learning algorithms for diabetes prediction. ICT Express, 7(4): 432-439. doi: 10.1016/j.icte.2021.02.004.
• Ayon, S., I., Islam, M., M. (2019). Diabetes prediction: a deep learning approach. International Journal of Information Engineering and Electronic Business, 12(2): 21. doi: 10.5815/ijieeb.2019.02.03.
• Baser, B., O., Yangın, M., Sarıdas, E., S. (2021). Classification of diabetes with machine learning techniques. Journal of Suleyman Demirel University Science Institute, 25(1): 112-120. doi: 10.19113/sdufenbed.842460.
• Er, M., B., Isık, I. (2021). Prediction of Diabetes disease using LSTM-based deep networks. Journal of Turkish Nature & Science, 10(1): 68-74.
• Kaggle, (accessed date: 16 February 2023). https://www.kaggle.com/code/kwonnnyr/diabetes-prediction-using-random-forest/notebook.
• Janiesch, C., Zschech, P., Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3): 685-695. doi: 10.1007/s12525-021-00475-2.
• Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11(2): 612-619.
• Kavzoglu, T., Colkesen, I. (2010). Classification of satellite images with decision trees. Electronic Journal of Map Technologies, 2(1):36-45.
• Suresh, A., Udendhran, R., Balamurgan, M. (2020). Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Computing, 24(11): 7947-7953. doi:10.1007/s00500-019-04066-4.
• Banjongkan, A., Pongsena, W., Kerdprasop, N., Kerdprasop, K. (2021). A study of job failure prediction at job submit-state and job start-state in high-performance computing system: using decision tree algorithms. Journal of Advances in Information Technology, 12(2). doi: 10.12720/jait.12.2.84-92.
• Shah, K., Patel, H., Sanghvi, D., Shah, M. (2020). A comparative analysis of logistic regression, random forest and KNN models for the text classification. Augmented Human Research, 5(1): 1-16. doi: 10.1007/s41133-020-00032-0.
• Asha Kiranmai, S., Jaya Laxmi, A. (2018). Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy. Protection and Control of Modern Power Systems, 3(1): 1-12. doi: 10.1186/s41601-018-0103-3.
• Gou, J., Ma, H., Ou, W., Zeng, S., Rao, Y., Yang, H. (2019). A generalized mean distance-based k-nearest neighbor classifier. Expert Systems with Applications, 115: 356-372. doi: 10.1016/j.eswa.2018.08.021.
• Kumbure, M., M., Luukka, P., Collan, M. (2020). A new fuzzy K-nearest neighbor classifier based on the Bonferroni mean. Pattern Recognition Letters, 140: 172-178. doi: 10.1016/j.patrec.2020.10.005.
• Asharf, J., Moustafa, N., Khurshid, N., Debie, E., Haider, W., Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electronics, 9(7): 1177. doi: 10.3390/electronics9071177.
• Dinh, T., V,. Nguyen, H., Tran, X., L., Hoang, N., D. (2021). Predicting rainfall-induced soil erosion based on a hybridization of adaptive differential evolution and support vector machine classification. Mathematical Problems in Engineering. 1-20. doi: 10.1155/2021/6647829.
• Guner, N., Comak, E. (2011). Predicting the success of engineering students in MathematicsI courses using support vector machines. Journal of Pamukkale University Engineering Science, 17(2): 87-96.
• Do, T., N. (2020). Automatic learning algorithms for local support vector machines. SN Computer Science, 1(1): 1-11. doi: 10.1007/s42979-019-0006-z
• Carta, S., Ferreira, A., Reforgiato Recupero, D., Saia, R. (2021). Credit scoring by leveraging an ensemble stochastic criterion in a transformed feature space. Progress in Artificial Intelligence, 10(4): 417-432.