A Review of Machine Learning Algorithms for Biomedical Applications.

Area of Science:

• Biomedical Data Science
• Computational Biology
• Machine Learning Applications

Background:

• The exponential growth in biomedical data necessitates advanced analytical tools.
• Machine learning (ML) offers powerful methods for creating predictive models from complex biological and clinical data.
• Understanding diverse ML methodologies is crucial for researchers and practitioners.

Purpose of the Study:

• To provide a comprehensive review of machine learning algorithms applied to biomedical data.
• To elucidate key ML concepts including supervised/unsupervised learning, feature selection, and evaluation metrics.
• To offer insights into emerging trends in ML for biomedical research and practice.

Main Methods:

• Analysis of major ML algorithms: decision trees, random forests, support vector machines, k-nearest neighbors.
• Review of dimensionality reduction techniques: principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE).
• Examination of prevalent neural network architectures: feedforward, convolutional (CNNs), and recurrent neural networks (RNNs).

Main Results:

• Detailed technical insights into the application and analysis of various ML methods in biomedical contexts.
• Demonstration of how dimensionality reduction techniques aid in visualizing and interpreting high-dimensional biomedical data.
• Overview of the specific roles and effectiveness of different neural network types in biomedical data analysis.

Conclusions:

• Machine learning provides essential tools for extracting meaningful insights from complex biomedical datasets.
• This review serves as a valuable reference for understanding and applying ML in biomedical research and clinical practice.
• Familiarity with ML algorithms is becoming indispensable for professionals in the biomedical field.