Hybrid Filtering-based Physician Recommender Systems using Fuzzy Analytic Hierarchy Process and User Ratings

As an emerging trend in data science, applications based on big data analytics are reshaping health informatics and medical scenarios.Currently, peoples are more cognizant and seek solutions to their healthcareproblems online. In the chorus, selecting a healthcare professional or organization is a tedious and time-consuming process. Patients may vainly spend time and meet severaldoctors until one is found that suits theirexact needs. Frequently, they do not have sufficient information on whereupon to base a decision. This has led to a dire requirementfor an efficient anddependablepatient-specific online tool to find out an appropriatedoctor in a limited time.In this paper, we propose a hybrid Physician Recommender System(PRS) by integrating various recommender approaches such asdemographic, collaborative, and content-based filtering for findingsuitabledoctors in line with the preferred choices of patients and their ratings. The proposed system resolves the problem of customization by studyingthe patient's criteriaforchoosing a physician. It employs an adaptive algorithm to find the overall rank of the particular doctor. Furthermore, this ranking method is applied to convert patients' preferred choices into a numerical base rating, which will ultimately be employed inour physician recommender system. The proposed system has been appraisedcarefully, and the result reveals that recommendations are rational and can satisfythe patient's need for consistentphysician selection successfully.