ARTIFICIAL INTELLIGENCE AND IOT INTEGRATION FOR REMOTE PATIENT MONITORING SYSTEMS: A FRAMEWORK FOR REAL-TIME HEALTH ANALYTICS AND EARLY ANOMALY DETECTION

Abstract

The growing prevalence of chronic diseases and the limitations of conventional hospital-based monitoring present a critical challenge in modern healthcare delivery. Patients with conditions such as hypertension, diabetes, cardiac disorders, and respiratory diseases require continuous physiological surveillance, yet periodic clinical visits fail to provide the real-time oversight necessary for timely intervention. This paper presents the design, development, and evaluation of an intelligent Remote Patient Monitoring (RPM) system that integrates Internet of Things (IoT)-based biosensors with Artificial Intelligence (AI) and Machine Learning (ML) techniques to enable continuous, automated, and clinically meaningful health monitoring. The proposed system collects physiological parameters including heart rate, blood pressure, oxygen saturation (SpO2), and body temperature via wearable IoT sensor nodes and transmits data through a multi-layer architecture encompassing edge, fog, and cloud computing tiers. At the analytics layer, a suite of ML models—including Long Short-Term Memory (LSTM) networks, Random Forest, and XGBoost classifiers—are trained on publicly available clinical datasets (MIMIC-III, PhysioNet) to detect health anomalies and predict adverse events with high accuracy. Experimental results demonstrate that the proposed LSTM-based anomaly detection model achieves an accuracy of 97.3%, a precision of 96.8%, recall of 97.1%, and an F1-score of 96.9%, outperforming existing benchmark approaches. The system further incorporates an automated, multi-channel alert notification module for real-time communication with patients, caregivers, and clinicians. The proposed framework addresses key challenges in healthcare accessibility, particularly for elderly populations, individuals with chronic conditions, and patients in geographically underserved regions. This work contributes a scalable, secure, and privacy-compliant AI-IoT architecture that holds significant potential for reducing hospital readmissions, lowering healthcare costs, and improving patient outcomes through proactive, data-driven clinical decision support.