Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals

Chin-Chuan Shih, Chi-Jie Lu, Gin-Den Chen and Chi-Chang Chang
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Chin-Chuan Shih: Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
Chi-Jie Lu: Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei 24205, Taiwan
Gin-Den Chen: Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
Chi-Chang Chang: School of Medical Informatics, Chung Shan Medical University & IT office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan

IJERPH, 2020, vol. 17, issue 14, 1-11

Abstract: Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes datasets from 19,270 patients, provided by an adult health examination program from 32 chain clinics and three special physical examination centers, between 2015 and 2019. There were 11 independent variables, and the glomerular filtration rate (GFR) was used as the predictive variable. The C4.5 decision tree algorithm outperformed the three comparison models for predicting early CKD based on accuracy, sensitivity, specificity, and area under the curve metrics. It is, therefore, a promising method for early CKD prediction. The experimental results showed that Urine protein and creatinine ratio (UPCR), Proteinuria (PRO), Red blood cells (RBC), Glucose Fasting (GLU), Triglycerides (TG), Total Cholesterol (T-CHO), age, and gender are important risk factors. CKD care is closely related to primary care level and is recognized as a healthcare priority in national strategy. The proposed risk prediction models can support the important influence of personality and health examination representations in predicting early CKD.

Keywords: early chronic kidney disease; machine learning; risk prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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