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

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  • Chin-Chuan Shih

    (Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
    General Administrative Department, United Safety Medical Group, New Taipei City 24205, Taiwan
    Deputy Chairman, Taiwan Association of Family Medicine, Taipei 24200, Taiwan
    These authors contributed equally to this work.)

  • Chi-Jie Lu

    (Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei 24205, Taiwan
    Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei 24205, Taiwan
    These authors contributed equally to this work.)

  • Gin-Den Chen

    (Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
    Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan)

  • Chi-Chang Chang

    (School of Medical Informatics, Chung Shan Medical University & IT office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan)

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.

Suggested Citation

  • Chin-Chuan Shih & Chi-Jie Lu & Gin-Den Chen & Chi-Chang Chang, 2020. "Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals," IJERPH, MDPI, vol. 17(14), pages 1-11, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:4973-:d:382799
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    References listed on IDEAS

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    Cited by:

    1. Chien-Lung Chan & Chi-Chang Chang, 2020. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 17(18), pages 1-7, September.
    2. Chin-Chuan Shih & Ssu-Han Chen & Gin-Den Chen & Chi-Chang Chang & Yu-Lin Shih, 2021. "Development of a Longitudinal Diagnosis and Prognosis in Patients with Chronic Kidney Disease: Intelligent Clinical Decision-Making Scheme," IJERPH, MDPI, vol. 18(23), pages 1-13, December.
    3. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.
    4. Yung-Chuan Huang & Yu-Chen Cheng & Mao-Jhen Jhou & Mingchih Chen & Chi-Jie Lu, 2023. "Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    5. Chi-Chang Chang & Chun-Chia Chen & Chalong Cheewakriangkrai & Ying Chen Chen & Shun-Fa Yang, 2021. "Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-Based Cancer Registry Study," IJERPH, MDPI, vol. 18(17), pages 1-9, August.

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