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- Title
A Review of Machine Learning Algorithms for Biomedical Applications.
- Authors
Binson, V. A.; Thomas, Sania; Subramoniam, M.; Arun, J.; Naveen, S.; Madhu, S.
- Abstract
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
- Subjects
MACHINE learning; SUPERVISED learning; RECURRENT neural networks; FEATURE selection; CONVOLUTIONAL neural networks; FEEDFORWARD neural networks; SUPPORT vector machines
- Publication
Annals of Biomedical Engineering, 2024, Vol 52, Issue 5, p1159
- ISSN
0090-6964
- Publication type
Article
- DOI
10.1007/s10439-024-03459-3