A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification

In the era of healthcare and its related research fields, dimensionality problem high-dimensional data is a massive challenge as it crucial to identify significant genes while conducting on diseases like cancer. As result, studying new Machine Learning (ML) techniques for raw gene expression biomedical an important field research. Disease detection, sample classification, early disease prediction are all analyses in bioinformatics. Recently, machine-learning have dramatically improved analysis high-dimension sets. Nonetheless, researchers’ studies faced vast dimensions, i.e., features (genes) with very low space. this paper, two-dimensionality reduction methods, feature selection, extraction introduced systematic comparison several dimension data. We presented review some most popular nature-inspired algorithms analyzed them. The paper mainly focused original principles behind each their applications cancer classification from Lastly, advantages disadvantages evaluated. This may guide researchers choose effective algorithm satisfactory