An AI-Driven Big Data Approach for Personalized Healthcare: Enhancing Predictive Analytics and Treatment Recommendations

Traditional data analytics methods face significant obstacles due to the growing amount of healthcare data from genetic databases, wearable technology, and Electronic Health Records (EHRs). This study presents a large data platform driven by AI that combines real-time data analysis with sophisticated machine learning models to improve individualised treatment. The system uses deep learning methods, including transformers, long short-term memory (LSTM) networks, and convolutional neural networks (CNNs) to give early disease identification, predictive analytics, and individualised therapy recommendations. With an average accuracy of 0.93 across several disorders, it helps manage chronic diseases. It provides good accuracy in predicting diabetes, heart disease, and hypertension. A 40% risk reduction and a 20%+ reduction in healthcare costs are the outcomes of the system's creation of customised treatment programs. The technology also guarantees scalability for realtime healthcare applications, meeting the demand for precise and effective decision assistance. According to comparative analysis, the suggested solution performs better than conventional machine learning models regarding cost effectiveness and predictive performance. This study<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> offers a promising first step towards more efficient, data-driven healthcare solutions by showing that AI-powered technologies may optimise healthcare resources and dramatically enhance patient outcomes.