Experimental disease prediction research on combining natural language processing and machine learning
AuthorsYu, Hong Qing
AffiliationUniversity of Bedfordshire
MetadataShow full item record
AbstractNowadays 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.
CitationYu HQ (2019) 'Experimental disease prediction research on combining natural language processing and machine learning', IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT) - Dalian, Institute of Electrical and Electronics Engineers Inc..
TypeConference papers, meetings and proceedings