An Effective Diagnostic Model for Personalized Healthcare Using Deep Learning Techniques

This chapter discusses a deep learning and IoE (Internet of Everything) based analytical model for disease detection, prediction and correct treatment for the patient would be proposed. In the proposed model, all the stakeholders, namely doctors, patients, medical staff within a clinic, hospital or a medical institute, would be embedded with micro-sensors. The sensors would in turn sense and capture the information gathered from these sources and the surrounding environment and then send it to a single repository, a base or a server, where it would be stored for further processing. These sensors produce massive amounts of data, which needs to be encrypted as well. Then, in order to improve the effectiveness and accuracy of prediction from the data received from these sensors, deep learning methods are used. Further, the advantages of the proposed model would be explored. To conclude, the limitations, opportunities and future applications of deep learning techniques would be discussed in this chapter.