DESIGN EFFICIENT CENTRALIZED PATIENT DATABASE SYSTEM FOR HEALTHY MONITORING OF DISEASE DIAGNOSIS IMPROVEMENT
Main Article Content
Keywords
Chat-bot, Collaborative Filter, Centralize, Healthcare Recommender System
Abstract
Rapid and reasonably priced e-healthcare data collecting and disease diagnosis are being progressively made possible by recent advancements in biotechnologies and high-performance computers. The accuracy of the model developed from the vast e-healthcare data is necessary for efficiency and dependability. Natural language processing (NLP) technology will be used by Health Bot to analyze and elaborate on the creation of an intelligent system that can support telemedicine services. The comprehensive, modular, and user-friendly platform called Health Bot seeks to enhance how patients interact with the healthcare system. The software can analyze and classify free text and voice input data to symptoms using NLP and speech recognition techniques. In order to anticipate the likelihood that a patient would develop a certain ailment and to alert the patient in the event of a disorder, free categorized data are utilized in the machine learning (ML) training process of the artificial intelligence (AI) models. • e Health Bot is operating as a virtual medical assistant to gather any data required for a medical interview, provide medical evaluations, schedule appointments with doctors, and monitor/record the patient's health.
References
2. R. Burke, A. Felfernig, and M. H. Göker, “Recommender structures: An overview,” AI Mag., vol. 32, pp. 13–18, 2011.
3. B. Stark, C. Knahl, M. Aydin, and K. O. Elish, “A literature overview on remedy recommender structures,” International Journal of Advanced Computer Science and Applications, 2019.
4. G. B. Gebremeskel, B. Hailu, and B. Biazen, “Architecture and optimization of information mining modeling for visualisation of information extraction: affected person protection care,” Journal of King Saud University-Computer and Information Sciences, 2019.
5. B. Stark, C. Knahl, M. Aydin, and K. Elish, “A literature overview on remedy recommender structures,” International magazine of superior laptop technological know-how and applications, vol. 10, no. 8, pp. 6–13, 2019.
6. Abugabah, A. A. AIZubi, F. Al-Obeidat, A. Alarifi, and A. Alwadain, “Data mining strategies for reading healthcare situations of city space-individual lung the usage of meta-heuristic optimized neural networks,” Cluster Computing, vol. 23, pp. 1781–1794, 2020.
7. H. Wang, F. Zhang, and M. Zhao, “Multi-assignment characteristic mastering for information graph better advice,” in Proceedings of the :e 2019 World Wide Web Conference, San Francisco, CA, USA, May 2019.
8. Y. Xiao, R. Xiang, and Y. Sun, “Personalized entity advice: a heterogeneous facts community approach,” in Proceedings of the seventh ACM International Conference on Web Search and Data Mining, ACM, New York, NY, USA, February 2014.
9. B. Hu, C. Shi, W. X. Zhao, and S. Y. Plillips, “Leveraging metapath primarily based totally context for top- n advice with a neural co-interest model,” in Proceedings of the twenty fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1531–1540, London, UK, August 2018.
10. H. Wang, F. Zhang, and J. Wang, “RippleNet: propagating person choices at the information graph for recommender structures,” in Proceedings of the twenty seventh ACM International Conference, Turin, Italy, October 2018.
11. H. Wang, M. Zhao, X. Xie, M. Gao, and W. Li, “Knowledge graph convolutional networks for recommender structures,” in Proceedings of the :e 2018 World Wide Web Conference, San Francisco, CA, USA, May 2019.
12. N. Zaman and J. Li, “Semantics-better advice machine for social healthcare,” in Proceedings of the 2014 IEEE twenty eighth International Conference on Advanced Information Networking and Applications, pp. 765–770, IEEE, Victoria, BC, Canada, May 2014.
13. Paramonov and A. Vasilyev, “Recommendation provider for clever space-primarily based totally customized healthcare machine, open improvements association,” in Proceedings of the 2016 nineteenth Conference of Open Innovations Association (FRUCT), IEEE, Jyvaskyla, Finland, November 2016.
14. S. B. Ahire and H. Khanuja, “HealthCare advice for customized framework,” International Journal of Computer Application, vol. 110, no. 1, pp. 24–26, 2015.
15. Archenaa and E. Anita, “Health recommender machine the usage of huge information analytics,” Journal of Management Science and Business Intelligence, vol. 2, 2017.
16. G. Guzm´an, M. R. Torres, and V. Tambonero, “A collaborative framework for sensing unusual coronary heart price primarily based totally on a recommender machine: semantic recommender machine for healthcare,” Journal of Medical and Biological Engineering, vol. 38, 2018.
17. H. Kaur, N. Kumar, and S. Batra, “An green multi-celebration scheme for privateness maintaining collaborative filtering for healthcare recommender machine,” Future Generation Computer Systems, vol. 86, pp. 297–307, 2018.
18. F. Ali, S. M. R. Islam, D. Kwak, P. Khan, N. Ullah, and S. Yoo, “Type-2 fuzzy ontology-aided advice structures for IoT-primarily based totally healthcare,” Computer Communications, vol. 119, pp. 138–155, 2018.
19. U. Somarathna, S. Walia, and L. Manchuri, “Recommendation machine for patron desired intellectual healthcare facility,” in Proceedings of the 2018 IISE Annual Conference, Orlando, FL, USA, May 2018.
20. K. Sahoo, C. Pradhan, R. K. Barik, and H. Dubey, “DeepReco: deep mastering primarily based totally fitness recommender machine. the usage of collaborative filtering,” Computation, vol. 7, no. 2, p. 25, 2019.