Machine Learning Model to Diagnose Diabetes Type 2 Based on Health Behavior

Abstract

Diabetes, in 2016, was the 7th death-causing disease in the world. It was the direct cause of 1.6 million deaths. In 2019, the number of adults (20-79 years) that were living with diabetes was approximately 463 million and is expected to rise to 700 million in 2045. The early diagnosis of diabetes will help treat it and prevent its complications. The need for an easy and fast way to diagnose diabetes is crucial. In this study, we are proposing a method to diagnose diabetes with the help of machine learning algorithms and tools. The proposed method utilizes the power of machine learning to create a model that can predict diabetes based on the health behavior of the patient. The model uses the relationship between a healthy lifestyle and diabetes. Our goal is to build a reliable machine learning model to predict diabetes, which will help significantly in easing and speeding up the diagnosing procedure of diabetes. We used modern machine learning algorithms like XGBoost, LightGBM, CatBoost, and artificial neural networks, and the dataset was obtained from the National Health and Nutrition Examination Survey (NHANES). In our study, the XGBoost algorithm performed the best with a Cross-Validation (10-fold) score of 0.864, and an overall accuracy of 87.7% for the validation dataset and 84.96% for the test dataset. 

Keywords

• Artificial intelligence
• Diabetes
• Health behavior
• Gradient boosting
• ANN

References

1. [1] https://www.healthline.com/health/diabetes. Access Date: 15.09.2019.
2. [2] https://www.diabetes.org/a1c/diagnosis. Access Date: 15.09.2019.
3. [3] Kasl, S. V., Cobb, S., “Health Behavior, Illness Behavior and Sick Role behavior”, Archives of Environmental Health: An International Journal, 12(2): 246-266, (1966).
4. [4] R. W. Rogers, S. Prentice-Dunn and D. S. Gochman, "Handbook of health behavior research 1: personal and social determinants", New York, NY, US: Plenum Press, Xxviii, 505: 113–132, (1997).
5. [5] Feldman, A. L., Long, G. H., Johansson, I., Weinehall, L., Fhärm, E., Wennberg, P., Rolandsson, O., "Change in lifestyle behaviors and diabetes risk: evidence from a population-based cohort study with 10 year follow-up", International Journal Of Behavioral Nutrition And Physical Activity, 14: 39, (2017).
6. [6] Gillies, C. L., Abrams, K. R., Lambert, P. C., Cooper, N. J., Sutton, A. J., Hsu, R. T., Khunti, K., "Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis", BMJ, 334: 299, (2007).
7. [7] Wareham, N. J., "Mind the gap: efficacy versus effectiveness of lifestyle interventions to prevent diabetes", The Lancet Diabetes & Endocrinology, 3: 160–161, (2015).
8. [8] Knowler, W. C., Barrett-Connor, E., Fowler, S. E., Hamman, R. F., Lachin, J. M., Walker, E. A., Nathan, D. M., “Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin”, The New England journal of medicine, 346(6): 393-403, (2002).

9. [9] Inzucchi, S. E., “Diagnosis of diabetes”, New England Journal of Medicine, 367(6): 542-550, (2012).
10. [10] Jaleel, J. A., Salim, S., Aswin R. B., "Computer aided detection of skin cancer", in 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), 1137-1142, (2013).
11. [11] Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., Tang, H., “Predicting diabetes mellitus with machine learning techniques”, Frontiers in genetics, 9: 515, (2018).
12. [12] Juneja, A., Juneja, S., Kaur, S., Kumar, V., “Predicting Diabetes Mellitus with Machine Learning Techniques Using Multi-Criteria Decision Making”, International Journal of Information Retrieval Research (IJIRR), 11(2): 38-52, (2021).
13. [13] Muhammad, L. J., Algehyne, E. A., Usman, S. S., “Predictive supervised machine learning models for diabetes mellitus”, SN Computer Science, 1(5): 1-10. 5, (2020).
14. [14] Tigga, N. P., Garg, S. “Prediction of type 2 diabetes using machine learning classification methods”, Procedia Computer Science, 167: 706-716, (2020).
15. [15] https://deepai.org/machine-learning-glossary-and-terms/machine-learning. Access Date: 25.10.2019.
16. [16] https://www.expert.ai/blog/machine-learning-definition/. Access Date: 25.10.2019.
17. [17] https://towardsdatascience.com/ensemble-methods-baggingboosting-and-stacking-c9214a10a205. Access Date: 26.10.2019.
18. [18] https://developers.google.com/machine-learning/data-prep/construct/samplingsplitting/imbalanced-data. Access Date: 24.01.2020.
19. [19] Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., Herrera, F., “Learning from imbalanced data sets”, Volume 11: 82-117, Berlin: Springer, (2018).
20. [20] https://towardsdatascience.com/handling-imbalanced-datasets-in-deeplearning-f48407a0e758. Access Date: 22.10.2019.
21. [21] https://www.cdc.gov/nchs/nhanes/index.htm. Access Date: 18.12.2020.
22. [22] Knowles, J. W., Reaven, G. “Usual blood pressure and new-onset diabetes risk: evidence from 4.1 million adults and a meta-analysis”, Journal of the American College of Cardiology, 67(13): 1656-1657, (2016).
23. [23] https://link.springer.com/referenceworkentry/10.1007%2F978-0387-30164-8_752. Access Date: 18.12.2020.
24. [24] https://link.springer.com/referenceworkentry/10.1007%2F978-0387-30164-8_770. Access Date: 18.12.2020.
25. [25] https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_645. Access Date: 18.12.2020.
26. [26] https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_582. Access Date: 18.12.2020.
27. [27] https://scikitlearn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score. Access Date: 18.12.2020.
28. [28] https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_28. Access Date: 18.12.2020.
29. [29] Chicco, D., Tötsch, N., Jurman, G., “The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation”, BioData Mining, 14, 13 (2021).