Department of Healthcare Administration and Medical Informatics, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
School of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan.
Int J Environ Res Public Health. 2022 Jan 22;19(3):1219. doi: 10.3390/ijerph19031219.
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 ( < 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.
性别是预测慢性肾脏病（CKD）的一个重要危险因素，但对其研究不足。本研究旨在探讨性别差异是否会影响早期 CKD 预测的危险因素。本研究使用了来自 19270 例成人健康筛查的数据，其中包括 5101 例 CKD 患者，筛选出 11 个独立变量作为危险因素，并使用七种机器学习技术训练预测模型，测试统计卡方检验变量的显著影响。性能指标包括分类准确率、灵敏度、特异性和精度。使用三种提取方法解决不平衡类别问题：手动采样、合成少数过采样技术和 SpreadSubsample。卡方检验显示性别、年龄、尿中红细胞计数、尿蛋白（PRO）含量和 PRO-尿肌酐比值具有统计学显著差异（<0.001）。在分类器预测性能方面，对于男性，手动提取方法、逻辑回归表现出最高的平均预测准确率（0.8053），而对于女性，手动提取方法、线性判别分析表现出最高的平均预测准确率（0.8485）。正常或异常 PRO-尿肌酐比值的临床特征表明，PRO 比值、年龄和尿红细胞计数是预测男女 CKD 的最重要危险因素。因此，本研究提出了一种具有可接受预测精度的预测模型。该模型支持医生的诊断和治疗，实现了早期发现和治疗的目标。本研究基于循证医学，使用机器学习方法开发预测模型。该模型已被证明可以尽可能支持早期临床 CKD 风险的预测，以提高临床决策的疗效和质量。
