An Intelligent Recommender System for People Who Are Prone to Fatty Liver Disease Publisher



Khademzadeh S^{1, 3} ; Ghazisaeidi M¹ ; Toosi MN² ; Roshanpoor A³ ; Mehraeen E⁴ Show Affiliations
Source: Informatics in Medicine Unlocked Published:2023

Abstract

Introduction: Considering the growth of the outbreak of fatty liver disease, it seems necessary and needed to design and develop an intelligent recommender system in order to provide clinical recommendations and a healthy lifestyle and also to provide a suitable educational and counseling platform. In this regard, the present research was conducted in order to design, develop and evaluate an intelligent recommender system for people who are prone to non-alcoholic fatty liver disease. Materials and methods: The current research was of the applied-developmental type which was carried out in order to create a non-alcoholic fatty liver intelligent recommender system. In the stage of the knowledge base designing, the selected data elements were placed in the form of tables in the SQL software. In the stage of the system designing, first the physical and functional features of the system were drawn by UML diagram, then coding was done using C# programming language. At the final stage, the usability evaluation of the designed system was assessed using the System Usability Scale (SUS). Results: The data requirements and technical capabilities were identified in three areas: demographic, clinical and technical requirements. In order to create a recommender system, the Microsoft SQL Server database and C# programming languages were used in the asp.net environment in a reactive manner. The fatty liver intelligent recommender system designed in this study had an average score of 74.625 in the evaluation of SUS applicability. Therefore, the usability of the fatty liver intelligent recommender system was approved by experts. Conclusion: In this study, through the designed system, the researchers provided a platform to remotely communicate between the patient and the specialist and receive unlimited self-care recommendations. Investigating the effectiveness of using the current recommender system to advance treatment goals and follow up on the patient's condition is suggested for future studies. © 2023 The Authors