Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

Journal: IEEE journal of translational engineering in health and medicine
Published Date: Apr 15, 2021

Abstract

OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes.

Authors

  • Md Rashed-Al-Mahfuz
    Department of Computer Science and EngineeringUniversity of RajshahiRajshahi6205Bangladesh.
  • Abedul Haque
    Department of HematopathologyThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA.
  • Akm Azad
    iThree Institute, University of Technology SydneyNSW2007Australia.
  • Salem A Alyami
    Department of Mathematics and StatisticsImam Muhammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia.
  • Julian M W Quinn
    Bone Biology Divisions, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
  • Mohammad Ali Moni
    Bone Biology Divisions, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; The University of Sydney, School of Medical Sciences, Faculty of Medicine & Health, NSW 2006, Australia. Electronic address: mohammad.moni@sydney.edu.au.

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