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.
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References
Russel, S.J., Norvig, P.: Artificial Intelligence - A Modern Approach. Prentice Hall, Upper Saddle River (2003)
Jee, K., Kim, G.H.: Potentiality of big data in the medical sector: focus on how to reshape the healthcare system. Healthc. Inf. Res. 19(2), 79–85 (2013)
Zhang, H.: The optimality of Naive Bayes. In: Seventeenth International Florida Artificial Intelligence Research Society Conference, Miami Beach, Florida, USA (2005)
Metsis, V., Androutsopoulos, I., Paliouras, G.: Spam filtering with Naive Bayes - which Naive Bayes? In: CEAS 2006 - The Third Conference on Email and Anti-Spam, Mountain View, California, USA, 27–28 July 2006
John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers, pp. 338–345 (2013)
Essa, Y.M., Attiya, G., El-Sayed, A.: New framework for improving big data analysis using mobile agent. Int. J. Adv. Comput. Sci. Appl. 5(3), 25–32 (2014)
Gray, R.S., Cybenko, G.: Agent TCL: a flexible and secure mobile-agent system. In: Proceedings of the 1996 TCL/TK Workshop, pp. 9–23 (1999)
Jin, X., Zhou, W., Bie, R.: Multinomial event naive Bayesian modeling for SAGE data classification. Comput. Stat. 22(1), 133–143 (2007)
Robertson, S.: Understanding inverse document frequency: on theoretical arguments for IDF. J. Doc. 60(5), 503–520 (2004)
Baker, L.D., McCallum, A.K.: Distributional clustering of words for text classification. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 96–103 (2003)
Rogati, M., Yang, Y.: High-performing feature selection for text classification (2003)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2(1), 45–66 (2002)
McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization, vol. 62(2), pp. 41–48 (2001)
Nigam, K.: Using maximum entropy for text classification. In: IJCAI 1999 Workshop on Machine Learning for Information Filtering, pp. 61–67 (1999)
Saad, M.K., Ashour, W.: Arabic text classification using decision trees. In: International Workshop on Computer Science and Information Technologies, CSIT 2010 (2010)
Bouaziz, A., Dartigues-Pallez, C., da Costa Pereira, C., Precioso, F., Lloret, P.: Short text classification using semantic random forest. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 288–299. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10160-6_26
Han, E.-H., Karypis, G., Kumar, V.: Text categorization using weight adjusted k-nearest neighbor classification. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 53–65. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45357-1_9
Colas, F., Brazdil, P.: Comparison of SVM and some older classification algorithms in text classification tasks. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice. IFIP AICT, vol. 217, pp. 169–178. Springer, Boston (2006). https://doi.org/10.1007/978-0-387-34747-9_18
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Our work was supported by National High-tech R&D Program (863 Program No. 2015AA015403).
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Chen, X., Zeng, G., Zhang, Q., Chen, L., Wang, Z. (2018). Classification of Medical Consultation Text Using Mobile Agent System Based on Naïve Bayes Classifier. In: Long, K., Leung, V., Zhang, H., Feng, Z., Li, Y., Zhang, Z. (eds) 5G for Future Wireless Networks. 5GWN 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-72823-0_35
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