A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study.
Background: The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunatel...
| Published in: | Asian Pacific Island Nursing Journal Vol. 8; pp. 1 - 15 |
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| Main Authors: | , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
JMIR Publications Inc.
2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182462255&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182462255 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23736658 L94B jtl: Asian Pacific Island Nursing Journal issn: 23736658 maglogo: N pubinfo: dt: 2024 vid: 8 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 182462255 182462255 182462255 10.2196/48378 182462255 ppf: 1 ppct: 14 formats: tig: atl: A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study. aug: au: Liu, Pei Liu, Yijun Liu, Hao Xiong, Linping Mei, Changlin Yuan, Lei affil: Department of Mathematics and Physics, Second Military Medical University, Shanghai, China sug: subj: Random Forest Risk Assessment Renal Insufficiency, Chronic Risk Factors Prediction Models Human Nonexperimental Studies Male Female Insurance, Health Middle Age Aged Aged, 80 and Over China Health Screening Glomerular Filtration Rate Albuminuria Sex Factors Age Factors Obesity Retirement Funding Source Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunately remains unknown. Therefore, early detection and intervention are vital means to mitigate the treatment burden on patients and decrease disease progression. Objective: In this study, we investigated the advantages of using the random forest (RF) algorithm for assessing risk factors associated with CKD. Methods: We included 40,686 people with complete screening records who underwent screening between January 1, 2015, and December 22, 2020, in Jing'an District, Shanghai, China. We grouped the participants into those with and those without CKD by staging based on the glomerular filtration rate staging and grouping based on albuminuria. Using a logistic regression model, we determined the relationship between CKD and risk factors. The RF machine learning algorithm was used to score the predictive variables and rank them based on their importance to construct a prediction model. Results: The logistic regression model revealed that gender, older age, obesity, abnormal index estimated glomerular filtration rate, retirement status, and participation in urban employee medical insurance were significantly associated with the risk of CKD. On RF algorithm–based screening, the top 4 factors influencing CKD were age, albuminuria, working status, and urinary albumin-creatinine ratio. The RF model predicted an area under the receiver operating characteristic curve of 93.15%. Conclusions: Our findings reveal that the RF algorithm has significant predictive value for assessing risk factors associated with CKD and allows the screening of individuals with risk factors. This has crucial implications for early intervention and prevention of CKD. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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