Optimizing patient care with big data analytics and machine learning algorithms

Abstract The convergence of big data and machine learning (ML) heralds transformative capabilities in healthcare, addressing obstacles in utilizing extensive, sophisticated datasets from electronic health records (EHRs), medical imaging, and wearable technology. This research aims to improve symptomatic accuracy, personalize treatments, and optimize operational efficiency through prognosis ML models while addressing critical concerns such as data security, scalability, and interpretation. By integrating a range of datasets and reconstruct sophisticated algorithms like graph neural networks (GNNs) and reinforcement learning (RL) with healthcare restraints, the research study achieved disease prediction accuracy prodigious 90% and a 25% boost in patient adherence to treatment regimen. Privacy defense methods, including federated learning (FL), ensured compliance with protected health information regulations, while foremost execution showed a 30% improvement in the operational efficiency of the healthcare system. The outcomes emphasize the crucial integration of technological advancements with clinical pertinence, establishing a foundation for secure, scalable, and patient-oriented healthcare system solutions that enhance outcomes and drive standardized improvements.