Big Data-Based Smart Health Monitoring System: Using Deep Ensemble Learning

Human life has become smarter by utilizing big data, telecommunication technologies, and wearable sensors over pervasive computing to give better healthcare services. Big data is built with the possibility to improve the healthcare industry. Big data makes the interconnection between patients, wearable sensors, healthcare caregivers, and providers through the utilization of Information and Communication Technology (ICT) and software. Most of the economic challenges in developing countries are caused by the healthcare sector, which occurs predominantly due to the increasing population requiring more quality of care concerning older people. Older people need great attention and care as they lead with irreparable damages when a minor accident or insignificant disease occurs. Therefore, the necessity of implementing new technologies and tools has arisen to support senior citizens regarding their healthcare. Various advancements in wireless technology, miniaturization, computing power, and processing made diverse healthcare innovations that led to developing the connected medical devices. Hence, this proposal develops a new healthcare monitoring system for tracking the activities of elderly people, where the Hadoop MapReduce technique for parallel processing the large-sized data. The data collected as mentioned in the available datasets is performed by using the numerous wearable sensors fixed on the “subject’s left ankle, right arm, and chest” that are transformed to the cloud platform and also to the data analytics layer according to the Internet of Medical Things (IoMT) devices. The given input undergoes data splitting to produce tiny chunks. These small chunks of the input files are then considered as Map tasks. Here, in the map phase, the features are optimally selected by the Hybrid Dingo Coyote Optimization (HDCO). The combiner phase classifies the physical activities using the developed Deep Ensemble Learning (DEL) consisting of classifiers such as “Extreme Learning Machine (ELM), deep Convolutional Neural Network (CNN), Long short-term memory (LSTM), Deep Belief Network (DBN), and Deep Neural Network (DNN)”. The parameter tuning in these classifiers is done by the same HDCO. The reducer phase extracts data from different chunks by merging the same classes. The developed HDCO-DEL has secured 13.66%, 16.01%, 17.33%, 13.6%, and 14.01% better accuracy than ELM, CNN, LSTM, DBN, DNN, and HealthFog, respectively on second dataset. The comparison with existing methods shows its better performance and also predicts physical activities with overall high accuracy.