Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease

Abstract

Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.

Association of Gender, Age, Physiological, and Biochemical Parameters among Chronic Renal Failure Patients at Zawia Kidney Hospital

“…There was a preponderance of cases in men, 68.2%, with a mean age of 69.44 ± 7.2 years. M. On the other hand, this study was not in line with another study which pointed out that the number of renal failure females is more than males [10].…”

“…Renal failure age groups of 11-20 and 21-30 years had a lower incidence of kidney failure, while patients aged over 31year had higher frequency of kidney failure. Our findings were in line with other studies [9][10][11][12][13].…”

“…This study showed that the levels of urea and creatinine increased significantly in patients with renal failure compared to control. In accordance with our findings, alike findings found in previous work pointed out that the level of creatinine concentration in blood serum rises more than the normal rate suggesting a defect in kidney function [10]. Elevated serum urea and creatinine levels can be explained by the fact that decreased kidney function leads to reduced waste disposal, which leads to an increase in their concentration in Serum.…”

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

“…ML-based decision support tools have been developed across many aspects of CKD care across disease prevention, diagnosis, and treatment [7], fuelled by the growth in volume and variety of big data in nephrology and healthcare in general [9,10]. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression to ESKD may help to facilitate early disease prevention, assist with early care planning, and allocate resources for the most significant clinical benefit [11][12][13][14][15].…”

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

“…Notably, algorithms developed in the prediction and diagnosis of CKD development and progression to ESKD may help to facilitate early disease prevention, assist with early care planning, and allocate resources for the most significant clinical benefit (11)(12)(13)(14)(15).…”