A hybrid Fifth Generation based approaches on extracting and analyzing customer requirement through online mode in healthcare industry. (March 2023) - Hameed, Abdul Zubar
Highlights: Service based Healthcare industries are strengthening their modern tools in order to extract required information from the authenticated data provided by social media networks. Analytic technique Deep learning, NLP and Text Mining are important in determining proper interpretation of the text documents for analysis. The use of 5G technologies will greatly enhance performance levels by providing appropriate Information and Communication and Information Innovations (ICT) tools such as Machine Learning, predictive analytic techniques, and the IOT. Abstract: The proposed hybrid fifth Generation-based approaches are employed to analyze and investigate the feedback of 37, 855 patients from 65 hospitals worldwide and gathered through various online modes. The collected data was analyzed with various cloud-computing technologies such as Artificial Intelligence (AI) based algorithms like natural language pre-processing (NLP), text mining (TM), and sentiment analysis (SA). This work discusses the new opportunities in the healthcare sector and the capabilities that 5G influences can bring to the healthcare industry. According to a recent investigation, the overall assessment ranking agrees with the personal view (sentiment) rating analysis for the title and full element of the web-based feedback obtained from the patients of different hospitals worldwide through an online survey. There are five main categories in the healthcare sector for which patients give priority and expect good services. They are treatment, physical facilities, dependability, timely committed service, and responsiveness. This research makes theoretical and practical contributions to the interdisciplinary areas comprising computer science, data management, and healthcare services by utilizing recent analytic techniques to attain optimized results. Graphical abstract: Image, graphical abstract