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Experimental disease prediction research on combining natural language processing and machine learning

  • Hong Qing Yu
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Nowadays Artificial Intelligent (AI) technologies are applied widely in many different areas to assist knowledge gaining and decision-making tasks. Especially, health information system can get most benefits from the AI advantages. In particular, symptoms based disease prediction research and production became increasingly popular in the healthcare sector recently. Various researchers and organizations have turned their interest in using modern computational techniques to analyze and develop new approaches that can efficiently predict diseases with reasonable accuracy. In this paper, we propose a framework to evaluate the efficiency of applying both Machine Learning (ML) and Nature Language Processing (NLP) technologies for disease prediction system. As an example, we scraped a disease- symptom dataset with NLP features from one of the UK most trustable National Health Service (NHS) website. In addition, we will exam our data in depth having symptom frequency, similarity and clustering analysis. As result, we can see that the prediction can have a very positive efficient rate but still open issues need to be addressed.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 145-150

Publication milestones

  • Published - 20/01/2020

Publication status

Published - 20/01/2020

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781728132983

Publication IDs

  • handle.net: 10547/624767
  • Scopus: 85079103625

Host publication title

2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)

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