An Ensemble Model Health Care Monitoring System

Internet of things (IoT) is utilized to enhance conventional health care systems in several ways, including patient's disease monitoring. The data gathered by IoT devices is very beneficial to medical facilities and patients. The data needs to be secured against unauthorized modifications because of security and privacy concerns. Conversely, a variety of procedures are offered by block chain technology to safeguard data against modifications. Block chain-based IoT-based health care monitoring is thus a fascinating technical advancement that may aid in easing security and privacy problems associated withthe collection of data during patient monitoring. In this work, we present an ensemble classification-based monitoring system with a block-chain as the foundation for an IoT health care model. Initially, data generation is done by considering the diseases including chronic obstructive pulmonary disease (COPD), lung cancer, and heart disease. The IoT health care data is then preprocessed using enhanced scalar normalization. The preprocessed data was used to extract features such as mutual information (MI), statistical features, adjusted entropy, and raw features. The total classified result is obtained by averaging deep maxout, improved deep convolutional network (IDCNN), and deep belief network (DBN) ensemble classification. Finally, decision-making is done by doctors to suggest treatment based on the classified results from the ensemble classifier. The ensemble model scored the greatest accuracy (95.56%) with accurate disease classification at a learning percentage of 60% compared to traditional classifiers such as neural network (NN) (89.08%), long short term memory (LSTM) (80.63%), deep belief network (DBN) (79.78%) and GT based BSS algorithm (89.08%).