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Machine Learning (ML) in Medicine: Review, Applications, and Challenges
- Source :
- Mathematics, Vol 9, Iss 2970, p 2970 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.
- Subjects :
- medicine
treatment
Cleaning methods
machine learning (ML)
diagnosis
Human intelligence
business.industry
Computer science
General Mathematics
Supervised learning
artificial intelligence (AI)
Machine learning
computer.software_genre
Taxonomy (general)
Evaluation methods
QA1-939
Computer Science (miscellaneous)
Reinforcement learning
Unsupervised learning
Artificial intelligence
business
Raw data
Engineering (miscellaneous)
computer
Mathematics
Subjects
Details
- ISSN :
- 22277390
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Mathematics
- Accession number :
- edsair.doi.dedup.....793d82177398b13d3d838d3fbc3f9d89