Abstract

The recent progress in the Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing has revolutionized the traditional healthcare system, upgrading it into a smart healthcare system. Medical services can be enhanced by integrating essential technology such as IoT and AI. The integration of IoT and AI presents several prospects within the healthcare industry. In this research, a novel hybrid Deep Learning (DL) model called Binary Butterfly Optimization Algorithm with Stacked Non-symmetric Deep Auto-Encoder (BBOA-SNDAE) for HD (HD) prediction based on the Medical IoT technology. The key aim of the work is to categorize and predict HD utilizing clinical data with the BBOA-SDNAE model. Initially, the model is trained using the Cleveland and Statlog datasets. The input data is preprocessed and standardized utilizing the Min-Max normalization. After preprocessing, the selection of features is performed utilizing the BBOA to choose the best optimal features for improved classification. Based on the selected features, the classification is performed using the SNDAE technique. The research model was assessed based on accuracy, sensitivity, precision, specificity, NPV, and F-measure. The model attained 99.62% accuracy, 99.45% precision, 99.32% NPV, 99.56% sensitivity, 99.45% specificity, and 99.38% f-measure using the HD dataset, and the model attained 98.84% accuracy, 98.73% precision, 98.34% NPV, 98.62% sensitivity, 98.21% specificity, and 98.27% f-measure using the sensor data. The results of the research model were compared with the current model for validation, where the research model outperformed all the compared models.