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

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 reg...

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Bibliographic Details
Published in:International journal of environmental research and public health 2020-07, Vol.17 (14), p.4973
Main Authors: Shih, Chin-Chuan, Lu, Chi-Jie, Chen, Gin-Den, Chang, Chi-Chang
Format: Article
Language:English
Subjects:
Algorithms
Blood pressure
Cholesterol
Classification
Complications
Creatinine
Data mining
Discriminant analysis
Disease prevention
Erythrocytes
Glomerular filtration rate
Health care
Hypertension
Independent variables
Kidney diseases
Kidneys
Learning algorithms
Lipoproteins
Medical prognosis
Neural networks
Patients
Prediction models
Proteinuria
Risk analysis
Risk factors
Triglycerides
Variables
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph17144973