Machine learning in medical applications: A review of state-of-the-art methods.

Journal: Computers in biology and medicine
Published Date:

Abstract

Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.

Authors

  • Mohammad Shehab
    Information Technology, The World Islamic Sciences and Education University. Amman, Jordan. Electronic address: moh.shehab12@gmail.com.
  • Laith Abualigah
    Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan.
  • Qusai Shambour
    Department of Software Engineering, Al-Ahliyya Amman University, Amman, Jordan. Electronic address: q.shambour@ammanu.edu.jo.
  • Muhannad A Abu-Hashem
    Department of Geomatics, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah, Saudi Arabia. Electronic address: mabohasm@kau.edu.sa.
  • Mohd Khaled Yousef Shambour
    Department of Scientific Information and Services, Umm Al-Qura University, Mecca, Saudi Arabia. Electronic address: myshambour@uqu.edu.sa.
  • Ahmed Izzat Alsalibi
    Information System College, Israa University, Gaza-Palestine, Jordan. Electronic address: asalibi@israa.edu.ps.
  • Amir H Gandomi
    Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.