Development of a Regression Model for Prediction of Chronic Kidney Disease Risk

Sonal Saini; Ajay Shanker Singh; Alok Katiyar

doi:10.52756/ijerr.2024.v45spl.023

Authors

Sonal Saini School of Computer Science and Engineering, Galgotias University, Greater Noida, India https://orcid.org/0009-0007-9575-4137
Ajay Shanker Singh School of Computer Science and Engineering, Galgotias University, Greater Noida, India https://orcid.org/0000-0001-8963-6141
Alok Katiyar School of Computer Science and Engineering, Galgotias University, Greater Noida, India https://orcid.org/0000-0002-1645-6585

DOI:

https://doi.org/10.52756/ijerr.2024.v45spl.023

Keywords:

Kidney disease, regression modeling, risk prediction, data pre-processing, mathematical modeling, optimization

Abstract

In recent years, chronic kidney disease (CKD) has been widespread in public health. Therefore, the early prediction of these diseases can save many lives. Keeping this fact in mind, this study presents a new way to predict CKD using regression modeling, aiming to improve early detection and save lives. For this purpose, the first authors collected the data of 104 patients, then re-arranged them in ten different parameters and calculated their scores. Thereafter, a composite CATH score is calculated as an output variable. Then, a suitable regression model will be identified based on various parameters such as R-squared, Adjusted R-squared, and PRESS values. Thereafter, to identify the significance of the selected model, the authors performed an Analysis of Variances (ANOVA) at a confidence interval of <0.05. Results revealed that the developed model has a higher degree of fitness and is suitable for prediction purposes. Finally, the authors performed parameter analysis to identify the effects of various parameters on CKD.

References

Bai, Q., Su, C., Tang, W., & Li, Y. (2022). Machine learning to predict end stage kidney disease in chronic kidney disease. Scientific reports, 12(1), 8377. https://doi.org/10.1038/s41598-022-12316-z

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

Chien, K.L., Lin, H.J., Lee, B.C., Hsu, H.C., Lee, Y.T., & Chen, M.F. (2010). A prediction model for the risk of incident chronic kidney disease. The American Journal of Medicine, 123(9), 836-846. https://doi.org/10.1016/j.amjmed.2010.05.010

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

Echouffo-Tcheugui, J.B., & Kengne, A.P. (2012). Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Medicine, 9(11), e1001344. https://doi.org/10.1371/journal.pmed.1001344

Hassan, M.N., Mahmud, M.S., Nipa, K.F., & Kamrujjaman, M. (2023). Mathematical Modeling and Covid-19 Forecast in Texas, USA: a prediction model analysis and the probability of disease outbreak. Disaster Medicine and Public Health Preparedness, 17, e19. https://doi.org/10.1017/dmp.2021.151

Hosseini Sarkhosh, S.M., Hemmatabadi, M., & Esteghamati, A. (2023). Development and validation of a risk score for diabetic kidney disease prediction in type 2 diabetes patients: a machine learning approach. Journal of Endocrinological Investigation, 46(2), 415-423. https://doi.org/10.1007/s40618-022-01919-y

Jiang, W., Wang, J., Shen, X., Lu, W., Wang, Y., Li, W., Gao, Z., Xu, J., Li, X., Liu, R., & Zheng, M. (2020). Establishment and validation of a risk prediction model for early diabetic kidney disease based on a systematic review and meta-analysis of 20 cohorts. Diabetes Care, 43(4), 925-933. https://doi.org/10.2337/dc19-1897

Khan, A., Qureshi, M., Daniyal, M., & Tawiah, K. (2023). A novel study on machine learning algorithm?based cardiovascular disease prediction. Health & Social Care in the Community, 2023(1), 1406060. https://doi.org/10.1155/2023/1406060

Krishnamurthy, S., Ks, K., Dovgan, E., Luštrek, M., Gradišek Pileti?, B., Srinivasan, K., Li, Y.C., Gradišek, A., & Syed-Abdul, S. (2021). Machine learning prediction models for chronic kidney disease using national health insurance claim data in Taiwan. In Healthcare, 9(5), 546. https://doi.org/10.3390/healthcare9050546

Liu, P., Sawhney, S., Heide-Jørgensen, U., Quinn, R.R., Jensen, S.K., Mclean, A., Christiansen, C.F., Gerds, T.A., & Ravani, P. (2024). Predicting the risks of kidney failure and death in adults with moderate to severe chronic kidney disease: multinational, longitudinal, population based, cohort study. BMJ, 385. https://doi.org/10.1136/bmj-2023-078063

Matsushita, K., Jassal, S.K., Sang, Y., Ballew, S.H., Grams, M.E., Surapaneni, A., Arnlov, J., Bansal, N., Bozic, M., Brenner, H., & Brunskill, N.J. (2020). Incorporating kidney disease measures into cardiovascular risk prediction: development and validation in 9 million adults from 72 datasets. E-Clinical Medicine, 27. https://doi.org/10.1016/j.eclinm.2020.100552

Nelson, R.G., Grams, M.E., Ballew, S.H., Sang, Y., Azizi, F., Chadban, S.J., Chaker, L., Dunning, S.C., Fox, C., Hirakawa, Y., & Iseki, K. (2019). Development of risk prediction equations for incident chronic kidney disease. JAMA, 322(21), pp.2104-2114. https://doi.org/10.1001/jama.2019.17379

Ningthoujam, S.S., Nath, R., Sarker, S.D., Nahar, L., Nath, D., & Talukdar, A.D. (2024). Prediction of medicinal properties using mathematical models and computation, and selection of plant materials. In Computational Phytochemistry, pp. 91-123. https://doi.org/10.1016/B978-0-443-16102-5.00011-0

Ozcan, M. and Peker, S., 2023. A classification and regression tree algorithm for heart disease modeling and prediction. Healthcare Analytics, 3, p.100130. https://doi.org/10.1016/j.health.2022.100130

Schena, F.P., Anelli, V.W., Trotta, J., Di Noia, T., Manno, C., Tripepi, G., D’Arrigo, G., Chesnaye, N.C., Russo, M.L., Stangou, M., & Papagianni, A. (2021). Development and testing of an artificial intelligence tool for predicting end-stage kidney disease in patients with immunoglobulin A nephropathy. Kidney International, 99(5), 1179-1188. https://doi.org/10.1016/j.kint.2020.07.046

Sawhney, R., Malik, A., Sharma, S., & Narayan, V. (2023). A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease. Decision Analytics Journal, 6, 100169. https://doi.org/10.1016/j.dajour.2023.100169

Singh, V., Asari, V.K., & Rajasekaran, R. (2022). A deep neural network for early detection and prediction of chronic kidney disease. Diagnostics, 12(1), 116. https://doi.org/10.3390/diagnostics12010116

Tangri, N., Stevens, L.A., Griffith, J., Tighiouart, H., Djurdjev, O., Naimark, D., Levin, A., & Levey, A.S. (2011). A predictive model for progression of chronic kidney disease to kidney failure. JAMA, 305(15), 1553-1559. https://doi.org/10.1001/jama.2011.451

Wang, S., Chen, Y., Cui, Z., Lin, L., & Zong, Y. (2024). Diabetes Risk Analysis Based on Machine Learning LASSO Regression Model. Journal of Theory and Practice of Engineering Science, 4(01), 58-64. https://doi.org/10.53469/jtpes.2024.04(01).08

Xiao, J., Ding, R., Xu, X., Guan, H., Feng, X., Sun, T., Zhu, S., & Ye, Z. (2019). Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. Journal of Translational Medicine, 17, 1-13. https://doi.org/10.1186/s12967-019-1860-0

Yang, H., Lin, X., Li, J., Zhai, Y., & Wu, J. (2023). A review of mathematical models of COVID-19 transmission. Contemporary Mathematics, pp.75-98. https://doi.org/10.37256/cm.4120232080