Examining the Machine Learning Approaches for Identifying Significant Proteins in a Cancer Disease: A Systematic Literature Review

Abstract

Previously, the medical field has struggled to identify suitable tools or systems to assist in researching and identifying significant proteins for specific diseases.However, recent advancements in artificial intelligence (AI) techniques, particularly in machine learning approaches, have led to researchers exploring this area.The primary objective of identifying significant proteins is to comprehend protein interactions and discover those that play pivotal roles in certain diseases especially cancer.By employing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and random forest, the healthcare sector can effectively work towards achieving these goals.In this systematic literature review, we collected, screened, and evaluated 42 articles published between 2013-2023, with a focus on identifying the most prominent machine learning algorithms utilized in this domain.The selection process involved considering articles with a Schimago Journal Rank score to ensure relevance and quality.

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