Explainable AI in big data intelligence of community detection for digitalization e-healthcare services

Abstract

Recommender Systems are designed to analysis the available data in the system to predict user’s desires and provide appropriate personalized suggestions to each user that suits their interests. In this paper, we have developed an explainable medical recommender system that uses graph concepts to provide an interpretable approach to medical data. The presented approach is based on community detection algorithms. It forms a graph between the users based on their similarity scores. Individuals with common interests are then grouped using graph community detection algorithms. Two community detection algorithms have been applied on the graphs of users and physicians in our medical recommender system. The results of applying two community detection algorithms are then used to address the cold start problem. We have identified the most influential users using a graph-based technique that finds the overlapping communities. We claim that using the overlapping graph of communities to address cold start problem will enhance the accuracy of the recommendations. Weighting or voting systems are also applied on the selected users to give feedback to potential consumers where there are n different options in a cluster. The similarity score of the users in the overlapping communities has been used to weight the final recommendation. The accuracy of recommended services depends on the proper selection of target populations. The proposed approach outperforms the use of each one of the community detections separately. The accuracy and precision of the proposed method are 93.06 and 88.34, which exceed the highest achieved accuracy in the literature.