Integrating Neural Network-Based Predictive Analytics with IoT for Real-Time Health Monitoring and Responsible Deployment

The combination of neural network-based predictive analytics is the focus of this study, which introduces a novel approach to IoT-based real-time health monitoring systems. Using techniques such as RSME, SVM, and LSTM, an exhaustive investigation of the performance of prediction models are conducted. The proposed model demonstrates significant improvements in accuracy, with a $\mathbf{9 8 . 2 5 \%}$ LSTM accuracy, an $\mathbf{8 3 . 7 3 \%}$ RSME accuracy, and an $86.25 \%$ SVM accuracy. The findings of this study demonstrate that, in comparison to the currently used models, the accuracy of LSTM is greater while maintaining competitive performance in RSME and SVM. Integrating neural network-based predictive analytics with the IoT enables responsible deployment in healthcare applications, improving accuracy and reliability in real-time health monitoring. To enhance patient care and well-being, this endeavor contributes to advancing the area of health monitoring systems based on the IoT by providing technologically sound solutions.