Leveraging Machine Learning Algorithms For Real-Time Health Risk Assessment And Personalized Treatment In The Us Healthcare System

Abstract

The pace of progress in machine learning (ML) algorithms are luminously promising a better future for healthcare delivery, specifically in terms of real time health risk evaluation and tailor-made treatment. This research paper aims to examine the utilization of ML method in enhancing predictive analytics, enhancing clinical decision processes, and optimizing patient safety in US healthcare. This study proposes an integrative framework for early detection of health risks and personalized treatments based on analyzing large-scale patient data, such as medical histories, genetic profiles, and real-time health indicators. The study further discusses the issues of data privacy, algorithmic bias and system integration, as well as the contribution of regulatory standards in upholding ethical implementation. Discursive techniques through case studies and review of existing trap models, the paper seeks to demonstrate not only the ability of ML in enhancing the accuracy of diagnoses, but also how it can add to cost effective and efficient delivery of healthcare in the USA. The results highlight the paradigm shifting ability of ML in revolutionizing health care by providing personalized, proactive care that enhances patient outcomes and maximizes efficient use of resources.