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Chiu YL, Jhou MJ, Lee TS, Lu CJ, Chen MS. Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease. Risk Manag Healthc Policy 2021; 14:4401-4412. [PMID: 34737657 PMCID: PMC8558038 DOI: 10.2147/rmhp.s319405] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/30/2021] [Indexed: 01/02/2023] Open
Abstract
PURPOSE As global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan's population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations. METHODS This study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group - a major health screening center in Taiwan - including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors. RESULTS The LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods. CONCLUSION The five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research.
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Affiliation(s)
- Yen-Ling Chiu
- Graduate Institue of Medicine and Graduate School of Biomedical Informatics, Yuan Ze University, Taoyuan, 32003, Taiwan, Republic of China
- Graduate Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei, 10002, Taiwan, Republic of China
- Department of Medical Research, Department of Medicine,Far Eastern Memorial Hospital, New Taipei, 22056, Taiwan, Republic of China
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei, 242062, Taiwan, Republic of China
| | - Tian-Shyug Lee
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei, 242062, Taiwan, Republic of China
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, 242062, Taiwan, Republic of China
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei, 242062, Taiwan, Republic of China
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, 242062, Taiwan, Republic of China
- Department of Information Management, Fu Jen Catholic University, New Taipei City, 242062, Taiwan, Republic of China
| | - Ming-Shu Chen
- Department of Healthcare Administration,College of Healthcare and Management, Asia Eastern University of Science and Technology, New Taipei, 22061, Taiwan, Republic of China
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Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, Hobbs FDR. Global Prevalence of Chronic Kidney Disease - A Systematic Review and Meta-Analysis. PLoS One 2016; 11:e0158765. [PMID: 27383068 PMCID: PMC4934905 DOI: 10.1371/journal.pone.0158765] [Citation(s) in RCA: 2083] [Impact Index Per Article: 260.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 06/21/2016] [Indexed: 12/11/2022] Open
Abstract
Chronic kidney disease (CKD) is a global health burden with a high economic cost to health systems and is an independent risk factor for cardiovascular disease (CVD). All stages of CKD are associated with increased risks of cardiovascular morbidity, premature mortality, and/or decreased quality of life. CKD is usually asymptomatic until later stages and accurate prevalence data are lacking. Thus we sought to determine the prevalence of CKD globally, by stage, geographical location, gender and age. A systematic review and meta-analysis of observational studies estimating CKD prevalence in general populations was conducted through literature searches in 8 databases. We assessed pooled data using a random effects model. Of 5,842 potential articles, 100 studies of diverse quality were included, comprising 6,908,440 patients. Global mean(95%CI) CKD prevalence of 5 stages 13·4%(11·7-15·1%), and stages 3-5 was 10·6%(9·2-12·2%). Weighting by study quality did not affect prevalence estimates. CKD prevalence by stage was Stage-1 (eGFR>90+ACR>30): 3·5% (2·8-4·2%); Stage-2 (eGFR 60-89+ACR>30): 3·9% (2·7-5·3%); Stage-3 (eGFR 30-59): 7·6% (6·4-8·9%); Stage-4 = (eGFR 29-15): 0·4% (0·3-0·5%); and Stage-5 (eGFR<15): 0·1% (0·1-0·1%). CKD has a high global prevalence with a consistent estimated global CKD prevalence of between 11 to 13% with the majority stage 3. Future research should evaluate intervention strategies deliverable at scale to delay the progression of CKD and improve CVD outcomes.
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Affiliation(s)
- Nathan R. Hill
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Samuel T. Fatoba
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jason L. Oke
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jennifer A. Hirst
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Daniel S. Lasserson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - F. D. Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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