Machine Learning Techniques for Modelling and Predicting the Influence of Kefir in a Low-Protein Diet on Kidney Function

Main Article Content

Vesna Knights Elena Damjanovska Gavriloska

Abstract

Introduction: This study aims to investigate the effects of a low-protein diet supplemented with kefir on protein catabolism and kidney function in stable chronic kidney disease patients. By employing advanced machine learning techniques, this research will explore the potential impact of kefir as a probiotic fermented dairy product on kidney function within the recommended intake. The study seeks to understand whether kefir supplementation in a low-protein diet can help maintain kidney function and identify any potential benefits or limitations.


Methods: The study employed a dataset comprising kidney health indicators and kefir intake records collected from 150 randomly selected patients in stage G1 to G5 during one year. Data preprocessing was performed to ensure data quality and feature relevance. Subsequently, a range of machine learning algorithms, including decision trees, random forests, and neural networks, but also and Stochastic Gradient Boosting and XgBoost model were implemented to model and predict the impact of kefir on kidney function.


The clinical data:  age, sex, blood pressure (systolic and diastolic), BMI, albumin, bacteria, level of blood glucose, hemoglobin, creatinine, urea, UNAPCR, protein intake and kefir, GFR, MDRD, proturija, the existence of hypertension, diabetes mellitus, coronary artery disease, stage of CKD at the beginning and CKD stage after 12 months, binary output (patient stay in a same stage or is reduced kidney function).


Results: The analysis results revealed promising predictive capabilities of the machine learning models, demonstrating associations between kefir consumption and kidney function. Binary output indicates the patient stayed in the same CKD stage using low-protein diet where source of protein is kefir.


Conclusion: This research underscores the value of machine learning techniques in modeling and predicting the impact of kefir on kidney function. By shedding light on potential associations, this study paves the way for further investigations into the role of kefir in kidney health and sets a precedent for future studies in this area.

Keywords: Kefir, low protein diet, chronic kidney disease, machine learning technique, Modeling and the Prediction

Article Details

How to Cite
KNIGHTS, Vesna; GAVRILOSKA, Elena Damjanovska. Machine Learning Techniques for Modelling and Predicting the Influence of Kefir in a Low-Protein Diet on Kidney Function. Medical Research Archives, [S.l.], v. 12, n. 7, july 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5631>. Date accessed: 15 nov. 2024. doi: https://doi.org/10.18103/mra.v12i7.5631.
Section
Research Articles

References

1. Rees, K., Dyakova, M., Wilson, N., Ward, K., Thorogood, M., Brunner, E. (2013). Dietary advice for reducing cardiovascular risk. Cochrane Database of Systematic Reviews, 2013(12). https://doi.org/10.1002/14651858.CD002128.pub5
2. Santesso, N., Bianchi, M., Mente, M., Mustafa, R., Heels-Ansdell, D., Schünemann, H. J. (2012). Effects of higher versus lower protein diets on health outcomes: A systematic review and meta-analysis. European Journal of Clinical Nutrition, 66(12). https://doi.org/10.1038/ejcn.2012.37
3. Levin, A., Hemmelgarn, B., Culleton, B., Sheldon Tobe, S., McFarlane, P., Ruzicka, M., Tonelli, M. (2008). Guidelines for the management of chronic kidney disease. Canadian Medical Association Journal, 179(11). https://doi.org/10.1503/cmaj.080351
4. Carrero, J., Cozzolino, M. (2014). Nutritional therapy, phosphate control and renal protection. Nephron Clinical Practice, 126(1), 1-7.
5. Mitch, W. (2005). Beneficial responses to modified diets in treating patients with chronic kidney disease. Kidney International, 67(1), 133-135.
6. Kaysen, G., Odabaei, G. (2013). Dietary protein restriction and preservation of kidney function in chronic kidney disease. Blood Purification, 35(1-3), 22-25.
7. Fouque, D., Laville, M., Boissel, P. J. (2009). Low protein diets for chronic kidney disease in nondiabetic adults. Cochrane Database of Systematic Reviews, 2009(3). https://doi.org/10.1002/14651858.CD001892.pub2
8. Fouque, D., Wang, P., Laville, M., Boissel, J. P. (2000). Low protein diets delay end-stage renal disease in nondiabetic adults with chronic renal failure. Nephrology, Dialysis, Transplantation, 15(12), 1986-1992.
9. Gavrilovska, E., Knights, V., Simovska, V., Ivanovski, N. (2022). Statistical analysis concerning the importance of a low protein diet in the progression of chronic kidney disease. Journal of Hygienic Engineering and Design, 39(1), 116-121.
10. Damjanovska Gavriloska, E., Kalevska, T., Dimitrovska, G., Kljusurić, J. G. Antoska Knights, V. (2023). Mathematical modelling of determining the dynamics of the nutritional value content of classic kefir and three types of flavors of functional kefir. Horizons - International Scientific Journal, 1(1), 116-129. https://doi.org/10.20544/
11. Damjanovska Gavriloska, E., Kalevska, T., Dimitrovska, G., Gajdoš Kljusurić, J. (2023). Sensory and pH evaluations of novel varieties of kefir. Horizons - International Scientific Journal, 1(1), 77-90. https://doi.org/10.20544/
12. Gligorova Damjanovska, E., Severova, G., Cakalaroski, K., Antovska-Knight, V., Danilovska, I., Simovska, V., Ivanovski, N. (2018). Beneficial short term effect of low protein diet on chronic kidney disease progression in patients with chronic kidney disease stage G3a. A pilot study. Hippokratia, 22(4), 178-182. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251848/
13. Chen, F., Kantagowit, P., Nopsopon, T., Chuklin, A., Pongpirul, K. (2023). Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance. PLoS ONE, 18(2), e0278729. https://doi.org/10.1371/journal.pone.0278729
14. Knights, V., Kolak, M., Markovikj, G., Gajdoš Kljusurić, J. (2023). Modeling and optimization with artificial intelligence in nutrition. Applied Sciences, 13(13), 7835.
15. Segal, Z., Kalifa, D., Radinsky, K., et al. (2020). Machine learning algorithm for early detection of end-stage renal disease. BMC Nephrology, 21(518). https://doi.org/10.1186/s12882-020-02093-0
16. Badrouchi, S., Bacha, M. M., Hedri, H., et al. (2023). Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation. Journal of Nephrology, 36, 1087-1100. https://doi.org/10.1007/s40620-022-01529-0
17. Al-Lamki, R., Burlacu, A., Iftene, A., Jugrin, D., Popa, I. V., Lupu, P. M., ... Covic, A. (2020). Using artificial intelligence resources in dialysis and kidney transplant patients: A literature review. BioMed Research International, 2020, 9867872. https://doi.org/10.1155/2020/9867872
18. Dritsas, E., & Trigka, M. (2022). Machine learning techniques for chronic kidney disease risk prediction. Big Data and Cognitive Computing, 6(3), 98. https://doi.org/10.3390/bdcc6030098
19. Zhao, J., Zhang, Y., Qiu, J., Zhang, X., Wei, F., Feng, J., ... Li, W.-D. (2022). An early prediction model for chronic kidney disease. Scientific Reports, 12, 2765. https://doi.org/10.1038/s41598-022-06665-y
20. Debal, D. A., & Sitote, T. M. (2022). Chronic kidney disease prediction using machine learning techniques. Journal of Big Data, 9(109). https://doi.org/10.1186/s40537-022-00657-5
21. Knights, V., & Prchkovska, M. (2024). From equations to predictions: Understanding the mathematics and machine learning of multiple linear regression. Journal of Mathematical & Computer Applications, 3(2), 1-8.
22. Devika, R., Avilala, S. V., & Subramaniyaswamy, V. (2019). Comparative study of classifier for chronic kidney disease prediction using naive Bayes, KNN and random forest. In S. Tiwari, M. C. Trivedi, M. L. Kolhe, K. Mishra, B. K. Singh (Eds.), Advances in Data and Information Sciences (pp. 679-684). Springer. https://doi.org/10.1109/ICCMC.2019.8819654
23. Saha, I., Gourisaria, M. K., & Harshvardhan, G. M. (2022). Classification system for prediction of chronic kidney disease using data mining techniques. In S. Tiwari, M. C. Trivedi, M. L. Kolhe, K. Mishra, B. K. Singh (Eds.), Advances in Data and Information Sciences (pp. 423-428). Springer. https://doi.org/10.1007/978-981-16-5689-7_38
24. Sinha, P., & Sinha, P. (2015). Comparative study of chronic kidney disease prediction using KNN and SVM. International Journal of Engineering Research & Technology (IJERT), 4(12). https://www.ijert.org/research/comparative-study-of-chronic-kidney-disease-prediction-using-knn-and-svm-IJERTV4IS120622.pdf
25. Youse, M. (2023). Prediction of chronic kidney disease using different classification algorithms: A comparative study. Journal of Xi’an Shiyou University, Natural Science Edition, 17(10), 453-462. http://xisdxjxsu.asia
26. Saif, D., Sarhan, A. M., & Elshennawy, N. M. (2024). Deep-Kidney: An effective deep learning framework for chronic kidney disease prediction. Health Information Science and Systems, 12(3). https://doi.org/10.1007/s13755-023-00261-8
27. Ghosh, S. K., & Khandoker, A. H. (2023). A machine learning driven nomogram for predicting chronic kidney disease stages 3–5. Scientific Reports, 13, 21613. https://doi.org/10.1038/s41598-023-48815-w
28. Maroni, B. J., Steinman, T. I., & Witch, W. E. (1985). A method for estimating nitrogen intake of patients with chronic renal failure. Kidney International, 27, 58-65.
29. American Heart Association. (n.d.). Understanding blood pressure readings. Retrieved May 31, 2024, from https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings
30. Mayo Clinic. (n.d.). Albumin test. Retrieved May 31, 2024, from https://www.mayoclinic.org/tests-procedures/albumin/about/pac-20384948
31. National Library of Medicine. (n.d.). Urine test. MedlinePlus. Retrieved May 31, 2024, from https://medlineplus.gov/ency/article/003772.htm
32. American Diabetes Association. (n.d.). Diagnosis. Retrieved May 31, 2024, from https://www.diabetes.org/a1c/diagnosis
33. National Kidney Foundation. (n.d.). Creatinine. Retrieved May 31, 2024, from https://www.kidney.org/atoz/content/creatinine
34. National Kidney Foundation. (n.d.). Proteinuria. Retrieved May 31, 2024, from https://www.kidney.org/atoz/content/proteinuria