Advancements in Machine Learning Applications for The Pharmaceutical, Biomedical, And Healthcare Industries

This paper investigates the developments in machine learning (ML) applications within the pharmaceutical industry along with biomedical and healthcare sectors. ML demonstrates its crucial impact through biomarker identification and improvements in drug discovery and diagnostic accuracy. The biomedical sector has implemented multiple ML algorithms, including Support Vector Machines (SVMs) and Random Forests (RFs), to detect microRNA (miRNA) biomarkers for cancer with excellent classification performance. Automated learning tools, including BioAutoML, have optimized feature extraction and model selection to surpass traditional methods regarding predictive performance. The pharmaceutical sector has benefited from ML-integrated high-content screening platforms that have resulted in the discovery of new antibacterial agents. Metrics such as the lowest effective dose (LOED) have broadened the scope of antibiotic discovery. The paper demonstrates how ML brings transformative enhancements to efficiency and accuracy along with innovative advancements in critical industry sectors.