IoT-Cloud-Centric Smart Healthcare Monitoring System for Heart Disease Prediction Using a Gated-Controlled Deep Unfolding Network with Crayfish Optimization

The rising incidence of heart disease requires effective and robust prediction algorithms, especially in Internet of Things (IoT)-cloud-based smart healthcare frameworks. This study presents a novel method for forecasting cardiovascular disease using superior data preprocessing, feature selection, and deep learning techniques. First, preprocessing is done using the Z-score min–max normalization technique to ensure consistent data scaling and standardize the dataset. After preprocessing, an innovative hybrid feature selection technique that combines Black Widow Optimization (BWO) and Influencer Buddy Optimization (IBO) is utilized. By achieving equilibrium between invention and execution, the BWO-IBO technique enhances feature selection and extracts the most pertinent information for heart disease prediction. The Gates-Controlled Deep Unfolding Network (GCDUN), which is based on the Crayfish Optimization Algorithm (COA), is an innovative framework for prediction. Through the use of a gates-controlled mechanism and a COA component that speeds up network parameter tuning based on crayfish behavior, GCDUN-COA increases feature representation and enhances the decision plane. The fusion of the IoT and a cloud-based framework takes the present data collection, processing, and remote monitoring a notch higher, thus making the system highly scalable and efficient for clinical use. When predicting cardiac disease, the method recommended shows improved F1-score, specificity, accuracy, recall, and precision continuously achieving above 99% across all performance metrics. By providing prompt diagnosis and intervention via an intelligent, adaptive prediction system, an IoT-driven cloud-based medical technology has the potential to revolutionize cardiac care.