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

• Published in: Computers in biology and medicine Vol. 145; p. 105458
• Main Authors: Shehab, Mohammad, Abualigah, Laith, Shambour, Qusai, Abu-Hashem, Muhannad A., Shambour, Mohd Khaled Yousef, Alsalibi, Ahmed Izzat, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-06-2022; Elsevier Limited
• Subjects: Algorithms; Brain; Brain research; Cancer; Convulsions & seizures; Datasets; Decision making; Diagnosis; Diagnostic systems; Forecasting; Gene expression; Healthcare; Heart failure; Industrial applications; Learning algorithms; Machine Learning; Mammography; Medical applications; Medical diagnosis; Medical field; Medical imaging; Medical research; Methods; Neuroimaging; Open source software; Patients; Public domain; R&D; Reproducibility of Results; Research & development; Security; State-of-the-art reviews; Support vector machines; Healthcare; Medical field; Diagnosis; Medical applications; Machine learning
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105458

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.
•A comprehensive review of Machine Learning (ML) techniques in the medical field is presented.•Standard technologies and how they affect medical diagnosis are highlighted.•Five major medical applications are deeply discussed focusing on the ML models.•This survey provides valuable references and guidance for researchers.