Constructing a Model with Binary Response to Some of the Factors Affecting the Incidence of Chronic Kidney Failure

Nabaa Naeem Mahdi

Abstract


Chronic kidney failure has become a disease of widespread diseases and increasingly in our society and must take precautions and prevention of causes and identify the most critical factors that lead to the disease. Since the binary logistic regression response (0,1) handles such as the model, the researcher took a sample consisting of 100 people between infected and non-infected chronic kidney. The researcher considered three factors that affect the disease: sex, age, and blood pressure. The model of logistic regression was constructed, and the parameter was estimated using the Maximum likelihood. The significance of the parameter was tested through the Wald test, and the full estimated model was tested with quality testing. The researchers recommend using logistics models for flexible use and application in the medical, economic, and social fields. Study of a larger number of variables affecting the disease and linear Multi-collinearly through the method of partial least square regression. Spreading awareness and culture among the community in identifying the causes of the disease and how to reduce treatment spread. We reached that the pressure is a factor, which is in line with the medical terms of interpretation that the blood pressure affecting the likelihood of developing high blood pressure leads significantly to chronic kidney failure, and the low blood pressure leads to the disease at a low rate. Besides, the sex variable also has a role in this model and the probability of injury for males more than females injured, but age did not appear significant to these parameters.

Keywords


Logistic regression; odd ratio; Wald test; logit transformation; maximum likelihood.

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References


Stanifer, J. W., Maro, V., Egger, J., Karia, F., Thielman, N., Turner, E. L., & Patel, U. D. (2015). The epidemiology of chronic kidney disease in Northern Tanzania: a population-based survey. PloS one, 10(4), e0124506.â€

Ahmed, R. M., & Alshebly, O. Q. (2019). Prediction and factors affecting of chronic kidney disease diagnosis using artificial neural networks model and logistic regression model. Iraqi Journal of Statistical Sciences, 16(28), 140-159.â€

Hasegawa, T., Sakamaki, K., Koiwa, F., Akizawa, T., Hishida, A., & CKD-JAC Study Investigators. (2019). Clinical prediction models for progression of chronic kidney disease to end-stage kidney failure under pre-dialysis nephrology care: results from the Chronic Kidney Disease Japan Cohort Study. Clinical and experimental nephrology, 23(2), 189-198.â€

Pencina, M. J., Parikh, C. R., Kimmel, P. L., Cook, N. R., Coresh, J., Feldman, H. I., ... & Star, R. A. (2019). Statistical methods for building better biomarkers of chronic kidney disease. Statistics in medicine, 38(11), 1903-1917.â€

Aitken, G. R., Roderick, P. J., Fraser, S., Mindell, J. S., O'Donoghue, D., Day, J., & Moon, G. (2014). Change in prevalence of chronic kidney disease in England over time: comparison of nationally representative cross-sectional surveys from 2003 to 2010. BMJ open, 4(9).â€

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., Yidris, N., & Fattahi, A. (2020). Experimental and numerical investigation of the mechanical behavior of full-scale wooden cross arm in the transmission towers in terms of load-deflection test. Journal of Materials Research and Technology, 9(4), 7937-7946.â€

Chen, Z., Zhang, X., & Zhang, Z. (2016). Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models. International urology and nephrology, 48(12), 2069-2075.â€

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., & Yidris, N. (2020). Conceptual design of the cross-arm for the application in the transmission towers by using TRIZ–morphological chart–ANP methods. Journal of Materials Research and Technology, 9(4), 9182-9188.â€

Molnar, M. Z., Kalantar-Zadeh, K., Lott, E. H., Lu, J. L., Malakauskas, S. M., Ma, J. Z., ... & Kovesdy, C. P. (2014). Angiotensin-converting enzyme inhibitor, angiotensin receptor blocker use, and mortality in patients with chronic kidney disease. Journal of the American College of Cardiology, 63(7), 650-658.â€

Sharaf, H. K., Salman, S., Dindarloo, M. H., Kondrashchenko, V. I., Davidyants, A. A., & Kuznetsov, S. V. (2021). The effects of the viscosity and density on the natural frequency of the cylindrical nanoshells conveying viscous fluid. The European Physical Journal Plus, 136(1), 1-19.â€

Vejakama, P., Ingsathit, A., McKay, G. J., Maxwell, A. P., McEvoy, M., Attia, J., & Thakkinstian, A. (2017). Treatment effects of renin-angiotensin aldosterone system blockade on kidney failure and mortality in chronic kidney disease patients. BMC nephrology, 18(1), 1-9.â€

Sharaf, H. K., Salman, S., Abdulateef, M. H., Magizov, R. R., Troitskii, V. I., Mahmoud, Z. H., ... & Mohanty, H. (2021). Role of initial stored energy on hydrogen microalloying of ZrCoAl (Nb) bulk metallic glasses. Applied Physics A, 127(1), 1-7.â€

Xiao, J., Ding, R., Xu, X., Guan, H., Feng, X., Sun, T., ... & Ye, Z. (2019). Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. Journal of translational medicine, 17(1), 1-13.â€

Salman, S., Hilo, A., Nfawa, S. R., Sultan, M. T. H., & Saadon, S. (2019). Numerical Study on the Turbulent Mixed Convective Heat Transfer over 2D Microscale Backward-Facing Step. CFD Letters, 11(10), 31-45.â€

Ashham, M. (2017). Simulation of Heat Transfer in a Heat Exchanger Tube with Inclined Vortex Rings Inserts. International Journal of Applied Engineering, 12(20), 9605-9613.â€

Flayyih, H. H., Mohammed, Y. N., Talab, H. R., & Radhi, N. R. (2020). Integration of the system of Activity Based Costing and liability accounting. Integration, 1(2), 1-9.â€

Alzabari, S. A. H., Talab, H. R., & Flayyih, H. H. (2019). The Effect of Internal Training and Auditing of Auditors on Supply Chain Management: An Empirical Study in Listed Companies of Iraqi Stock Exchange for the Period 2012-2015. Int. J Sup. Chain. Mgt Vol, 8(5), 1070.â€

Ashham, M., Aliywy, A. M., Raheemah, S. H., Salman, K., & Abbas, M. (2020). Computational Fluid Dynamic Study on Oil-Water Two Phase Flow in A Vertical Pipe for Australian Crude Oil. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 71(2), 134-142.â€

Talab, H. R., Flayyih, H. H., & Ali, S. I. (2017). Role of Beneish M-score model in detecting of earnings management practices: Empirical study in listed banks of Iraqi Stock Exchange. International Journal of Applied Business and Economic Research, 15(23), 287-302.â€

Hasan, R. F., & Mahdi, N. N. (2020). Robust non-parametric regression models for some petroleum products. Periodicals of Engineering and Natural Sciences, 8(1), 263-271.â€




DOI: http://dx.doi.org/10.18517/ijaseit.11.2.14092

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Published by INSIGHT - Indonesian Society for Knowledge and Human Development