Precision medicine is a huge step forward in healthcare because it focuses on
making personalised treatment plans for each person by looking at their genes, their
surroundings, and the decisions they make in their daily lives. Unlike the old “one-sizefits-all” method, precision medicine uses data science and Machine Learning (ML) to
deal with the different types of diseases, the different ways that drugs work, and the
complicated health data of each patient. Researchers and doctors have been able to find
trends in genetic, clinical, and imaging datasets by combining big data analytics,
prediction modelling, and advanced machine learning methods. This has led to more
accurate diagnosis, analysis, and treatment plans. This article discusses the significance
of machine learning approaches like Support Vector Machines (SVM), Random
Forests, Neural Networks, and grouping algorithms for the evaluation of large organic
datasets. Those techniques assist in identifying biomarkers, projecting treatment
outcomes, and enhancing therapeutic processes through simplicity. A case study in
cancer illustrates how ML models can be used to predict how patients will respond to
personalised treatments, find out genetic markers related to drug resistance, and make
sure that every patient receives the best treatment available. New kinds of illnesses
discovered via precision medicine applications enable clinicians to create more targeted
and successful treatment procedures. Precision medicine has great capability; its
implementation is difficult due to problems like record protection, the requirement of ethical AI models, and the incapacity of healthcare structures to engage with one
another. Data scientists, medical professionals, legislators, and regulatory authorities
must cooperate to get past these issues.
Keywords: Biomarker discovery, Data science, Machine learning, Precision medicine, Personalized healthcare, Predictive modelling.
