ABSTRACT
Recently, the Internet of Health Things (IoHT) is rising as a new idea in information technologies, intending to build a dynamic global network infrastructure by interconnecting a number of physical and virtual “things” with an increasing number of mobiles and sensors. Recommendation systems (RS) in healthcare offer relevant medical details to the user, which are highly related to the medical development of the patient associated with health records. This chapter aims to develop a medicine RS (MRS) by using data mining and deep learning methodologies. The presented MRS involves a set of components, namely database system, data preparation, RS method, model validation, and data visualization. For the RS model, a novel extreme learning machine (ELM) ensemble classifier, namely b-ELM, incorporates the Bag of Little Bootstraps concepts into the ELM. A mistake-check process has also been applied for ensuring diagnosis accuracy and service quality. The MRS model recommends drugs to the patients according to medical diagnosis data. The presented MRS model has been tested under several aspects and the results ensured the betterment of the proposed model. The obtained simulation outcome indicated that the MRS model has offered an effective recommendation of drugs with high accurateness.
