Abstract
Abstract: The realm of healthcare constantly seeks innovative methods to improve patient care and outcomes. The advent of predictive analytics in healthcare has opened new avenues for preemptive diagnosis and treatment strategies. This paper introduces a novel Patient Sickness Prediction System (PSPS), designed to leverage machine learning algorithms and big data analytics for early detection of potential health issues. By integrating electronic health records (EHRs), real-time health monitoring data, and patient history, the PSPS aims to provide healthcare professionals with actionable insights, facilitating timely and personalised care. This research explores the system's development, implementation strategies, and its potential impact on healthcare delivery and patient outcomes.
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