AI DRIVEN WBAN IN HEALTHCARE USING MACHINE LEARNING

The convergence of Artificial Intelligence (AI) and Wireless Body Area Networks (WBANs) is reshaping healthcare monitoring by enabling non-invasive, intelligent systems.This study presents a simulation-based framework for predicting vital signs-Electrocardiogram (ECG), blood pressure, and body temperature-using machine learning, thus eliminating reliance on physical sensor hardware.Synthetic datasets were generated to emulate WBAN conditions, and three supervised learning algorithms-Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF)-were employed for predictive analysis.Model performance was evaluated using Mean Squared Error (MSE) and the Coefficient of Determination (R score).Among the tested models, Random Forest demonstrated superior accuracy and robustness across all health parameters.The results highlight the potential of AI-driven simulation as a cost-effective and scalable solution for the development and preliminary testing of healthcare monitoring systems.Future efforts will aim to enhance model versatility with broader datasets and explore real-time implementation and anomaly detection capabilities.