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Development of a Longitudinal Diagnosis and Prognosis in Patients with Chronic Kidney Disease: Intelligent Clinical Decision-Making Scheme

Author

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  • Chin-Chuan Shih

    (Dean of the Lian-An Clinic, Taipei 24200, Taiwan
    Deputy Chairman, Taiwan Association of Family Medicine, Taipei 24200, Taiwan
    These authors contributed equally to this work.)

  • Ssu-Han Chen

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    These authors contributed equally to this work.)

  • Gin-Den Chen

    (Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan)

  • Chi-Chang Chang

    (Department of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
    Department of Information Management, Ming Chuan University, Taoyuan 33300, Taiwan)

  • Yu-Lin Shih

    (Department of Otolaryngology-Head and Neck Surgery, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan City 33305, Taiwan)

Abstract

Previous studies on CKD patients have mostly been retrospective, cross-sectional studies. Few studies have assessed the longitudinal assessment of patients over an extended period. In consideration of the heterogeneity of CKD progression. It’s critical to develop a longitudinal diagnosis and prognosis for CKD patients. We proposed an auto Machine Learning (ML) scheme in this study. It consists of four main parts: classification pipeline, cross-validation (CV), Taguchi method and improve strategies. This study includes datasets from 50,174 patients, data were collected from 32 chain clinics and three special physical examination centers, between 2015 and 2019. The proposed auto-ML scheme can auto-select the level of each strategy to associate with a classifier which finally shows an acceptable testing accuracy of 86.17%, balanced accuracy of 84.08%, sensitivity of 90.90% and specificity of 77.26%, precision of 88.27%, and F1 score of 89.57%. In addition, the experimental results showed that age, creatinine, high blood pressure, smoking are important risk factors, and has been proven in previous studies. Our auto-ML scheme light on the possibility of evaluation for the effectiveness of one or a combination of those risk factors. This methodology may provide essential information and longitudinal change for personalized treatment in the future.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12807-:d:695130
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    References listed on IDEAS

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    1. Justin B Echouffo-Tcheugui & Andre P Kengne, 2012. "Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review," PLOS Medicine, Public Library of Science, vol. 9(11), pages 1-18, November.
    2. 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.
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    Cited by:

    1. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.
    2. Hao-Yun Kao & Chi-Chang Chang & Chin-Fang Chang & Ying-Chen Chen & Chalong Cheewakriangkrai & Ya-Ling Tu, 2022. "Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease," IJERPH, MDPI, vol. 19(3), pages 1-11, January.

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