Artificial intelligence - based approaches based on random forest algorithm for signal analysis: Potential...

Random Forest (RF) is a powerful ensemble-based supervised machine learning technique that builds multiple decision trees using bootstrap aggregating and random feature selection to improve classification and regression accuracy while reducing overfitting. RF models can be trained on diverse datasets, including spectroscopic, electrochemical, imaging-based, and omics-derived signals, enabling applications in cell damage detection, toxicity assessment, diagnostic classification, and biochemical variable prediction. In this concise narrative review, we focus on recent applications of random forest machine learning models in physiology and biochemistry, with particular emphasis on the usefulness of these models in signal analysis and the detection of chemico-biological interactions in various experimental settings. We discuss the strengths and weaknesses of RF algorithms compared to other supervised machine learning approaches, as well as their potential applications in future biomedical projects and endeavors. Finally, we provide commentary on our own recently published work regarding the utilization of decision tree-based models for the prediction of cell damage based on fractal, textural, wavelet, and other indicators of two-dimensional signal structure.