An Interactive Web Engineering System for Syndrome Classification in Social Networks

Nowadays social networking has permeated through every human activity including, of course, web engineering. As social networks develop, a wide variety of social networking applications appear, which attract a lot of new users. In the current paper, an interactive web engineering system for syndrome classification is developed. The system is implemented in the context of social networks, using pattern recognition algorithms based on unconventional computing. The classification algorithm used is Morphological Autoassociative Max Memories, which belongs to the associative approach of Pattern Recognition. Throughout the experimental phase, the proposed system is applied to help diagnose several syndromes. The core proposal of this article is a web system that features an innovative characteristic: it interacts with social media users, enabling potential patients to utilize this novel system as a valuable tool for remote diagnosis of syndromes. It is crucial to emphasize, as demonstrated in the experimental results section, that the proposed interactive system achieves a high true positive rate compared to other state-of-the-art models. Experimental results confirm that the proposed web engineering system can be a valuable tool for knowledge discovery and social networking between different profiles of people involved in syndrome classification.