Integrating AI and IoT for Enhanced Predictive Healthcare Monitoring: A Comprehensive Study on Breast Cancer Patient-Centric Approach

In the contemporary healthcare landscape, various intelligent automated approaches are revolutionizing healthcare tasks. Learning concepts are pivotal for activities like comprehending acquired data and monitoring patient behavior. Amid patient-centric concerns, addressing data heterogeneity, extraction, and prediction challenges is crucial. To enhance patient monitoring using care indicators like cost and stay length, we propose an AI and IoT-integrated automated approach, while tackling heterogeneity. Employing certain rules for data extraction to form a distinct representation, our model integrates pre-treatment information and employs a modified Combined Random Forest, LSTM, and BiLSTM algorithm for predictive posttreatment monitoring. This framework, synergizing AI, IoT, and advanced neural networks, facilitates real-time health monitoring, especially focusing on breast cancer patients. Embracing pre-treatment, in-treatment, and post-treatment phases, our model aims for accurate diagnosis, improved cost-efficiency, and extended stays. The evaluation underscores scalability, reliability enhancement, and validates the framework's efficacy in transforming healthcare practices.