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
Permalink -

https://repository.derby.ac.uk/item/982vq/experimental-disease-prediction-research-on-combining-natural-language-processing-and-machine-learning

  • 40
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics
AlZoubi, A., Eskandari, A., Yu, H. and Du, H. 2024. Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics . Bioengineering. 11 (5), pp. 1-23. https://doi.org/10.3390/bioengineering11050453
Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network
Tao Li, Jiawei Yang, Chenxi Li, Lulu Lv, Kang Liu, Zhipeng Yuan, Youyong Li, Hongqing Yu and Yu, H. 2024. Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_4
Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare
Yu, H., Adebola Alaba and Ebere Eziefuna 2024. Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_2
Attention Enhanced Siamese Neural Network for Face Validation
Yu, H. 2023. Attention Enhanced Siamese Neural Network for Face Validation. Artificial Intelligence and Applications. 2 (1), pp. 21-27. https://doi.org/10.47852/bonviewAIA32021018
IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare
Qi, J., Yu, H., Yang, P., Yang, Y. and Pang, Z. 2023. IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare. 32nd ACM International Conference on Information and Knowledge Management (CIKM’23), Birmingham, UK. ACM. https://doi.org/10.1145/3583780.3615299
AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research
Yu, H., O'Neill, S. and Kermanizadeh, A. 2023. AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research. Bioengineering. 10 (10), pp. 1-18. https://doi.org/10.3390/bioengineering10101134
Learning Disease Causality Knowledge from Web of Health Data
Yu, H. and Reiff-Marganiec, S. 2022. Learning Disease Causality Knowledge from Web of Health Data. International journal on semantic web and information systems. 18 (1), pp. 1-19. https://doi.org/10.4018/IJSWIS.297145
Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System
Yu, H. 2020. Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System. in: Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3 New York Springer.
Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System
Handscombe, J. and Yu, H. 2019. Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System. Sensors. 19 (2), p. 347. https://doi.org/10.3390/s19020347
Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research
Yu, H. and Dong, F. 2019. Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research. International Journal of Web Engineering and Technology. 14 (2), pp. 103 - 121.
Mining Symptom and Disease Web Data with NLP and Open Linked Data
Yu, H. 2019. Mining Symptom and Disease Web Data with NLP and Open Linked Data. 5th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’19) Lisbon, Portugal – August, 2019. https://doi.org/10.11159/mvml19.108
A linear logic approach to the composition of RESTful web services
Zhao, X., Liu, E., Yu, H. and Clapworthy, G.J. 2015. A linear logic approach to the composition of RESTful web services. International Journal of Web Engineering and Technology. 10 (3), pp. 245-271. https://doi.org/10.1504/ijwet.2015.072348
Socio-semantic Integration of Educational Resources - the Case of the mEducator Project
Dietze, Stefan, Kaldoudi, Eleni, Dovrolis, Nikolas, Giordano, Daniela, Spampinato, Concetto, Hendrix, Maurice, Protopsaltis, Aristidis, Taibi, v and Yu, H. 2013. Socio-semantic Integration of Educational Resources - the Case of the mEducator Project. Journal of Universal Computer Science. 19 (11), pp. 1-27. https://doi.org/10.3217/jucs-019-11-1543
Interlinking educational resources and the web of data
Dietze, S., Sanchez‐Alonso, S., Ebner, H., Yu, H., Giordano, D., Marenzi, I. and Pereira Nunes, B. 2013. Interlinking educational resources and the web of data. Program. 47 (1). https://doi.org/10.1108/00330331211296312
Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning
Yu, H., Pedrinaci, C., Dietze, S. and Domingue, J. 2012. Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning. IEEE Transactions on Learning Technologies. 5 (2), pp. 130-142. https://doi.org/10.1109/tlt.2012.1
An automated approach to Semantic Web Services Mediation
Dietze, S., Gugliotta, A., Domingue, J., Yu, H. and Mrissa, M. 2010. An automated approach to Semantic Web Services Mediation. Service Oriented Computing and Applications. 4, p. 261–275. https://doi.org/10.1007/s11761-010-0070-7