Research Article
Classification of Medical Consultation Text Using Mobile Agent System Based on Naïve Bayes Classifier
@INPROCEEDINGS{10.1007/978-3-319-72823-0_35, author={Xingyu Chen and Guangping Zeng and Qingchuan Zhang and Liu Chen and Zhuolin Wang}, title={Classification of Medical Consultation Text Using Mobile Agent System Based on Na\~{n}ve Bayes Classifier}, proceedings={5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings}, proceedings_a={5GWN}, year={2018}, month={1}, keywords={Naive Bayes Medical big data Mobile agent Artificial intelligence}, doi={10.1007/978-3-319-72823-0_35} }- Xingyu Chen
Guangping Zeng
Qingchuan Zhang
Liu Chen
Zhuolin Wang
Year: 2018
Classification of Medical Consultation Text Using Mobile Agent System Based on Naïve Bayes Classifier
5GWN
Springer
DOI: 10.1007/978-3-319-72823-0_35
Abstract
Aiming at the interaction model of the Internet medical website, a classifier of medical text data based on Naive Bayes was proposed and realized in this paper. Once a user posed questions on the websites, this classifier would instantly classify the user’s questions and enable accurate question delivery. Furthermore, a data service platform was realized by taking advantages of mobile agent technology. With the service platform, companies could avoid considering the security of data when conducting data analysis. Finally, experiments were conducted according to the process of data analysis in the service platform. The experimental results showed: the proposed service platform was feasible, and a medical consultation text classifier with high accuracy was realized to improve user experience of medical websites.
