Comparing different supervised machine learning algorithms for disease prediction.

Journal: BMC medical informatics and decision making
Published Date: Dec 21, 2019

Abstract

BACKGROUND: Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.

Authors

  • Shahadat Uddin
    Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Room 524, SIT Building (J12), Darlington, NSW, 2008, Australia. shahadat.uddin@sydney.edu.au.
  • Arif Khan
    Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Room 524, SIT Building (J12), Darlington, NSW, 2008, Australia.
  • Md Ekramul Hossain
    Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Room 524, SIT Building (J12), Darlington, NSW, 2008, 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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