Abstract
Worldwide, epidemics continue to be a concern on public health. Even with the technological advances, there are still barriers present in predicting the outbreaks. We propose a new methodology known as FREEDOM (Effective Surveillance and Investigation of Water-borne Diseases from data-centric networking using Machine Learning) to perform effective surveillance and investigation of water-borne diseases from social media with next-generation data. In the proposed model, we collected the data from the Twitter media, preprocessed the tweet content, performed hierarchical spectral clustering, and generated the frequent word set from each cluster through the apriori algorithm. At last, the inferences are extracted from the frequent word set through human intervention. From the experimental results, the support and confidence value of the outcome derived from the Apriori algorithm has exhibited the different water-borne diseases that are not listed in the WHO (World Health Organization), and the surveillance of those diseases with percentage ranking and has been achieved using the data-centric networking. They get aligned with precise results portraying real statistics. This type of analysis will empower doctors and health organizations (Government sector) to keep track of the water-borne diseases, their symptoms for early detection, and safe recovery thereby sufficiently reducing the death tolls.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal on Artificial Intelligence Tools
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.