Chronic kidney disease diagnosis using decision tree algorithms.

Journal: BMC nephrology
PMID: 34372817

Abstract

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.

Authors

  • Hamida Ilyas
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan.
  • Sajid Ali
    Institute für pharmazeutische Technologie & Biopharmazie, Philipps University Marburg, Germany.
  • Mahvish Ponum
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan. mponum.msit15seecs@seecs.edu.pk.
  • Osman Hasan
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan.
  • Muhammad Tahir Mahmood
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan.
  • Mehwish Iftikhar
    Department of Mathematics, Comsats University Islamabad, Lahore Campus, 54000, Pakistan.
  • Mubasher Hussain Malik
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan.

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