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Framework of Health Recommender System for COVID-19 Self-assessment and Treatments: A Case Study in Malaysia


Mahfudzah Othman, Nurzaid Muhd Zain, Zulfikri Paidi, and Faizul Amir Pauzi


Vol. 21  No. 1  pp. 12-18


This paper proposes a framework for the development of the health recommender system, designed to cater COVID-19 symptoms’ self-assessment and monitoring as well as to provide recommendations for self-care and medical treatments. The aim is to provide an online platform for Patient Under Investigation (PUI) and close contacts with positive COVID-19 cases in Malaysia who are under home quarantine to perform daily self-assessment in order to monitor their own symptoms’ development. To achieve this, three main phases of research methods have been conducted where interviews have been done to thirty former COVID-19 patients in order to investigate the symptoms and practices conducted by the Malaysia Ministry of Health (MOH) in assessing and monitoring COVID-19 patients who were under home quarantine. From the interviews, an algorithm using user-based collaborative filtering technique with Pearson correlation coefficient similarity measure is designed to cater the self-assessment and symptoms monitoring as well as providing recommendations for self-care treatments as well as medical interventions if the symptoms worsen during the 14-days quarantine. The proposed framework will involve the development of the health recommender system for COVID-19 self-assessment and treatments using the progressive web application method with cloud database and PHP codes.


COVID-19, home quarantine, self-assessment, collaborative filtering, Pearson correlation coefficient, health recommender system