IoT-based smart healthcare using efficient data gathering and data analysis

An IoT-based savvy medical service is the utilization of interconnected shrewd gadgets and sensors, similar to wearable, trackers, and portable applications, to gather and send continuous wellbeing information to attendants and specialists. This advancement focuses on remote checking and patient clinical issues, fortunate reflections, and restructured care. Using Internet of Things (IoT) devices, patients can remotely monitor their health conditions and send real-time data to healthcare providers. The next step is to use cutting-edge analytical tools to find patterns in the data and potential health issues. This encourages more proactive and preventative care, allowing providers of medical services to mediate earlier issues and possibly avoid more serious ones. Additionally, data analysis can provide insight into population health patterns, assisting health authorities in making more informed decisions regarding asset distribution and disease prevention efforts. Our paper introduces a novel framework for early disease diagnosis in IoT-based smart healthcare systems, employing efficient data collection and analysis methods. Specifically, we delve into the modified Influence Buddy Optimization (MIBO) algorithm for energy-efficient clustering during data collection and the Sparrow Search-based Self-Tuning (S3T) model for cluster head selection under design constraints. Additionally, we propose the Fractal Interpolation with Convolution Neural Network (FI-CNN) hybrid deep learning model for disease classification and detection. To assess the effectiveness of our framework, we conduct extensive validation experiments employing various performance metrics to evaluate both analytical and data collection models. Notably, our proposed MIBO-S3T scheme exhibits remarkably low energy consumption, achieving a 52.43% reduction compared to existing schemes. This work aims to succinctly present the background, problem statement, methodology, and key findings of our work, providing readers with a comprehensive overview of our contribution to the field of IoT-based smart healthcare systems.