A Review of Machine Learning Algorithms for Biomedical Applications

• Published in: Annals of biomedical engineering Vol. 52; no. 5; pp. 1159 - 1183
• Main Authors: Binson, V. A., Thomas, Sania, Subramoniam, M., Arun, J., Naveen, S., Madhu, S.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical data; Biomedical Engineering and Bioengineering; Biomedical materials; Biomedicine; Biophysics; Classical Mechanics; Data analysis; Decision analysis; Decision trees; Embedding; Humans; Learning algorithms; Machine Learning; Medical research; Neural networks; Neural Networks, Computer; Prediction models; Principal components analysis; Recurrent neural networks; Review; Support Vector Machine; Support vector machines; Unsupervised learning; Deep learning; Dimensionality reduction methods; SVM; Convolutional neural networks; Machine learning; Biomedical
• ISSN: 0090-6964; 1573-9686; 1573-9686
• DOI: 10.1007/s10439-024-03459-3

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.