Vol. 8, Special Issue 3 (2019)

Author(s):
Pradeep Verma, Nikhil Mishra and Vishal Srivastava

Abstract:
Personalized medicine, a paradigm shift in healthcare, has gained momentum with the advent of advanced technologies, particularly in the realm of machine learning. This review paper explores the transformative role of machine learning in tailoring treatment strategies for individuals, revolutionizing the field of healthcare by leveraging the power of data analysis. The integration of patient-specific data, encompassing genomics, clinical history, and other omics data, forms the foundation for the development of personalized treatment plans. The review begins by providing a comprehensive overview of the current landscape of personalized medicine, emphasizing the limitations of traditional one-size-fits-all approaches. The discussion then delves into the key concepts of machine learning, elucidating how algorithms can extract meaningful patterns from vast and diverse datasets. Noteworthy machine learning techniques, such as deep learning, ensemble methods, and reinforcement learning, are scrutinized for their efficacy in predicting individual patient responses to diverse treatment modalities. Crucial to the success of personalized medicine is the identification of relevant biomarkers, which serve as integral components in treatment decision-making. The review highlights the pivotal role of machine learning in biomarker discovery, showcasing its ability to discern subtle patterns and relationships within multi-dimensional datasets. Keywords such as "feature selection," "dimensionality reduction," and "precision medicine" underscore the pivotal techniques that drive these discoveries.