Machine Learning-Driven Cutting-Edge Approach for Designing a Healthcare Chatbot

Authors

  • Ritu Shree Assistant Professor, Department of Computer Science & Application, Vivekananda Global University, Jaipur, India
  • Ajay Rastogi Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Garima Assistant Professor, Department of Data Science (DS), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Kalaiarasan C. Associate Dean, Department of Computer Science and Engineering, Presidency University, Bangalore, India

Keywords:

Healthcare, Chatbot, communication, grasshopper optimized spiking neural network (GO-SNN)

Abstract

The market for sophisticated Chatbot systems that can communicate with consumers and provide helpful information and support is rising, particularly in the healthcare sector. Developing a Chatbot for healthcare that can comprehend and reply to complicated medical questions in real-time, however, is still a complex undertaking. We suggest the grasshopper-optimized spiking neural network (GO-SNN) tackle this problem. The GO method is used to optimize the weight of synaptic and interconnectivity of the SNN, allowing the chatbot software to process data and make decisions effectively. Extensive tests are run utilizing a standard of actual health inquiries and a dataset containing medical texts from the Kaggle source to assess the efficacy of the suggested approach. The experiment's findings show that in terms of accuracy, reaction speed, and user happiness, the grasshopper-optimized SNN-based medical Chatbot performed better than the alternatives

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Published

11.07.2023

How to Cite

Shree, R. ., Rastogi, A. ., Garima, & C., K. . (2023). Machine Learning-Driven Cutting-Edge Approach for Designing a Healthcare Chatbot. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 198–205. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3041

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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