IOMT AND IODS IN HEALTH CARE FOR PULMONARY HEART DISEASE DETECTION USING DEEP LEARNING TECHNIQUE

Abstract

Living standards are largely determined by the availability of effective and high- quality medical care. In addition to accelerating disease detection and treatment, remote medical services provide the potential to reduce medical costs. A unified approach to connecting multiple devices, users, databases, etc. is possible with the Internet of Things (IoT). One form of IoT intended to support medical services is the Internet of Medical Things (IoMT). Many medical activities, including monitoring chronic diseases and diagnosing illnesses, can be completed remotely with the help of IoMT, which will save healthcare expenses and improve services. The Research work is based on using the Real World Data Set maintained by Kaggle. The data set is based on the thermal images of the patients captured from Drones using special- ized FLIR Camera. From data sets obtained, it has been observed there is increased rate in blood pressure, pulse rate, body temperature and respiratory rate within one month duration. From the obtained results it has been predicted that there is prob- ability of respiratory disease detection in patients moving inside and outside the hospital for 30 day’s duration time period. In this research work confusion matrix is developed, which is based on Deep learning technique. Simple Neural Network is used as Machine Leaarning and Deep Learning technique. The predicted results are very helpful in identifying the status of patient suffering from respiratory dis- ease. This situation is also very dangerous for the healthy people inside the hospital especially patients care takers and doctors and paramedical staff. The effected pa- tient should immediately recommended for PCR Lab test. In case result found to be positive the patient is recommended to admit in hospital