Diagnosis of Chronic Diseases Based on Patients’ Health Records in IoT Healthcare Using the Recommender System

Abstract

Due to the growth of IoT applications, especially health care, the information of patients’ health records using data collection from IoT-connected devices has been considered. Biological data of patients in the health record helps to monitor the patient’s status and identify various diseases. Chronic diseases are a type of silent disease that, if not diagnosed in time, can cause irreparable damage to patients. The use of patients’ medical record data for early diagnosis of chronic diseases has recently attracted the attention of many researchers. On the other hand, the application of machine learning methods in the form of recommender systems has taken an important step in improving medical services and health care. In this paper, a medical recommender system was presented to identify and treat chronic diseases using an IoT device. In the present method, the electronic patient health record dataset that is loaded in the PhysioNet data repository has been used. In the present dataset, patients’ health records have been recorded according to the identified diseases and the physician’s diagnosis. In the proposed method, the $K$ -nearest neighbor classification method is used to identify the type of disease, and the collaborative filtering method is used to find the appropriate treatment for patients. The results of the implementation of the proposed method show that this approach, based on the use of symptom similarity among patients, has good accuracy in diagnosing and predicting chronic diseases and has provided higher results than previous methods.