Chronic kidney disease diagnosis using decision tree algorithms.
Background: Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages bas...
| Publicado en: | BMC Nephrology Vol. 22; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
8/9/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151818515&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151818515 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712369 1CI6 jtl: BMC Nephrology issn: 14712369 maglogo: N pubinfo: dt: 8/9/2021 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 151818515 151818515 NLM34372817 10.1186/s12882-021-02474-z NLM34372817 151818515 ppf: 1 ppct: 11 formats: tig: atl: Chronic kidney disease diagnosis using decision tree algorithms. aug: au: Ilyas, Hamida Ali, Sajid Ponum, Mahvish Hasan, Osman Mahmood, Muhammad Tahir Iftikhar, Mehwish Malik, Mubasher Hussain affil: School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan sug: subj: Disease Progression Decision Trees Renal Insufficiency, Chronic Physiopathology Renal Insufficiency, Chronic Diagnosis Glomerular Filtration Rate Kidney Function Tests Methods Male Severity of Illness Indices Algorithms Prognosis Middle Age Reproducibility of Results Early Diagnosis Medical Records Statistics and Numerical Data Female Patient Classification Middle Aged: 45-64 years Male Female ab: Background: Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages.Methods: Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy.Results: Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest.Conclusions: The study concluded that it may be used to build an automated system for the detection of severity of CKD. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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