Integrating Machine Learning and Big Data Analytics for Personalized Medicine and Treatment Optimization

The integration of both machine learning (ML) and big data analytics (BDA) under the philosophy of precision medicine is positively dismantling healthcare to enhance individualized/patient-specific care/treatment regimens for individuals. Using large swathes of medical information from EHRs, genomics, continuous monitoring technologies, and the like, we want to derive fine-grained patient data, forecast disease trajectory, and tailor care interventions, and hence, more effective health outcomes. The current use of ML and BDA for personalized interventions is discussed comprehensively in this paper. Specifically, the paper provides an account of the algorithms and methods for data processing, together with the system architecture concerning various types of health data. Furthermore, we outline a whole system architecture and the suggested approach in which all these technologies can fit into. We discuss how the non-quantum definitions of performance measures, such as accuracy, precision, recall, and response time, are impacted by the deployment of personalized medicine systems based on ML. These are aspects of the beginning of a strong movement of integrated healthcare delivery, specially highlighted in the study.