Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning

Conference paper


Yu, H. 2020. Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning. IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE Xplore. https://doi.org/10.1109/iccsnt47585.2019.8962507
AuthorsYu, H.
TypeConference paper
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.

Keywordsartificial intelligence ; disease prediction ; NHS
Year2020
ConferenceIEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)
Journal2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)
PublisherIEEE Xplore
Digital Object Identifier (DOI)https://doi.org/10.1109/iccsnt47585.2019.8962507
Web address (URL)http://dx.doi.org/10.1109/iccsnt47585.2019.8962507
ISBN9781728132990
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8950250/proceeding
Output statusPublished
Publication dates20 Jan 2020
Publication process dates
Deposited15 Aug 2022
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