Machine Learning-Driven Predictive Analytics for Real-Time Smart Healthcare Monitoring and Adaptive Drug Delivery Systems

Abstract

The combination of Machine Learning (ML) and Internet of Things (IoT)-powered healthcare systems has transformed realtime patient monitoring and personalized drug delivery. The present paper introduces a detailed scheme of predictive analytics in intelligent healthcare systems that use physiological information gathered by wearable sensors to allow early identification of diseases and customized treatment. The suggested system will be based on the latest ML models like neural networks and adaptive learning algorithms to process continuous data on patients, anticipate health anomalies, and dynamically vary the delivery of drugs in real-time. The outcomes of the experiment show better accuracy, shorter response times, and better patient results than the conventional healthcare monitoring systems. Although the system has promising benefits, there are practical shortcomings, including issues of privacy of data, expensive implementation, the presence of constant connectivity, and generalization of models in heterogeneous groups of patients. The future directions involve incorporating federated learning as a privacy preservation tool, building low-power edge computing, and explanation-related AI to enhance clinical trust and transparency. Such developments will likely make the ML-driven smart healthcare systems more scalable, reliable, and acceptable.

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