Rough set based intelligent approach for identification of H1N1 suspect using social media
Keywords:
Twitter, Swine flu, Influenza, H1N1, Rough Sets, Text ClassificationAbstract
Social media data offer unique challenges and opportunities for monitoring and surveillance of public health. The identification of epidemic suspect depends on doctor’s experience, symptoms and laboratory tests. Delay in identifying the beginning of infectious epidemic results in a big damage to a society. To handle the cases of epidemic effectively, a low-cost, accurate and timely diagnosis system is needed. An intelligent technique based on Rough set theory for identifying suspect of H1N1 using social media, has been presented in this paper.
Classification of symptoms from the dataset has been performed using machine learning techniques. From the large number of symptom attributes mined from the dataset, H1N1 related symptom attributes, have been extracted. These extracted attributes contribute maximum to the decision-making process. Rough set theory has been used to evaluate significant attributes (symptoms) from symptom attribute set by generating reducts using indiscernibility relation. Identification of suspects is performed using significant conditional attributes and dependency rules generated from reducts. The utilization of presented social media based medical decision support system turn out to be an effective approach to assist government and health agencies in decision-making.
References
Apollo Health Services (2015). Apollo Health Services, India
(http://www.apollohealthcity.com /swine-flu/) [Accessed: 25-Sep-
.
Aramaki E., Maskawa S. & Morita M. (2011). Twitter catches the
Flu: detecting influenza epidemics using Twitter. Proceedings of the
Conference on Empirical Methods in Natural Language Processing,
Association for Computational Linguistics, Stroudsburg, PA, USA.
Bodnar, T. & Salathé, M. (2013).Validating models for disease
detection using Twitter. International World Wide Web Conference
Companion. Rio de Janeiro, Brazil.
CDCP (2015). Centers for Disease Control and Prevention (http://
www.cdc.gov /h1n1flu/qa.htm)[Accessed: 25-Sep-2015]
Ceron, A. C. (2013). Every tweet counts? How sentiment analysis
of social media can improve our knowledge of citizens’ political
preferences with an application to Italy and France. New Media &
Society, 16(2):340-358.
Chew, C. M. (2010). Pandemics in the age of twitter: A content
analysis of the 2009 h1n1 outbreak. Master’s thesis, University of
Toronto.
Emarketer (2015). Emarketer (http://www.emarketer.com)
[Accessed : 25-Sep-2015].
Glik, D. (2007). Risk communication for public health emergencies.
Annual Reviews of Public Health, 28:33-54.
Hassanien, A. E. & Ali, J.M.H. (2004). Rough set approach
for generation of classification rules of breast cancer data.
INFORMATICA, 15(1):23–38.
Hu, X., Lei, T. & Liu, H. (2011). Enhancing accessibility
of microblogging messages using semantic knowledge.20th
ACM International Conference on Information and Knowledge
Management, New York, NY, USA.
Hvidsten, T. R. (2013). A tutorial-based guide to the ROSETTA
system: A Rough Set Toolkit for Analysis of Data.
Jain,V. K. & Kumar, S. (2015). An effective approach to track
levels of influenza-A (H1N1) Pandemic in India Using Twitter.
Procedia Computer Science, 70(1):801–807.
Jia,X., Shang, L., Zhou, B. & Yao, Y. (2016). Generalized
attribute reduction in Rough set theory. Knowledge-Based Systems,
(1):204–218.
Jiye, Li. (2007). Rough set based rule evaluations and their
applications, Ph.D Thesis, University of Waterloo.
Khan, K., Ullah, A. & Baharudin, B. (2016). Pattern and semantic
analysis to improve unsupervised techniques for opinion target
identification. Kuwait Journal of Science, 43(1):129-149.
Kumara, S. U. & Inbaranib, H. H. (2015). A novel neighborhood
rough set based classification approach for medical diagnosis.
Procedia Computer Science, 47(1):351–359.
Lampos, V. & Cristianini, N. (2010). Tracking the flu pandemic
by monitoring the social web.In 2nd IAPR Workshop on Cognitive
Information Processing, IEEE Press.
Miao, D., Duan, Q., Zhang, H. & Jiao, N. (2009). Rough set
based hybrid algorithm for text classification. Expert Systems with
Applications, 36(5):9168–9174.
Moses, D. & Deisy, C. (2015). A survey of data mining algorithms
used in cardiovascular disease diagnosis from multi-lead ECG data.
Kuwait Journal of Science, 42(2):206-235.
Nallamuth, R. & Palanichamy, J. (2015). Optimized construction
of various classification models for the diagnosis of thyroid
problems in human beings. Kuwait Journal of Science, 42(2):189-
NHP(2015). National Health Portal of India,(http://www.nhp.gov.
in/diseaseaz/s/swineflu) [Accessed : 25-Sep-2015]
Pandey, P., Kumar, S. & Srivastava, S. (2013). Forecasting using
Fuzzy time series for diffusion of innovation: Case of Tata Nano car
in India. National Academy Science Letters, 36(3):299-309.
Pang, B. & Lee, L. (2008). Opinion mining and sentiment analysis.
Foundations and Trends in Information Retrieval, 2(1):1–135.
Parker, P., Wei, Y., Yates, A., Frieder , O. & Goharian, N. (2013).
A framework for detecting public health trends with Twitter.IEEE/
ACM International Conference on Advances in Social Networks
Analysis and Mining, Niagara Falls, ON, Canada.
Pawlak, Z. (1982). Rough sets. International Journal of Information
and Computer Science. 11(5):341-356.
Pawlak, Z. (1991). Rough sets: theoretical aspects of reasoning
about data. Theory and decision library.Kluwer Academic
Publishers. Norwell, MA, USA.
Peng, Y., Kou, G., Shi, Y. & Chen, Z.X. (2008). A descriptive
framework for the of data mining and knowledge discovery.
International Journal of Information Technology & Decision
Making, 7(4):639-682.
PIB(2015). Preventive measures for Swine flu(http://pib.nic.in/
newsite/PrintRelease.aspx? relid=115710) [Accessed: 15-Aug-
.
Qian, Y. H., Liang, J.Y., Li, D.Y. et al. (2008). Measures for
evaluating the decision performance of a decision table in rough set
theory. Information Sciences, 178(1):181-202.
Santos, J.C. & Matos, S. (2014). Analysing Twitter and web
queries for flu trend prediction. Theoretical Biology and Medical
Modelling, 11(1):1-11.
Stewart, A. & Diaz, E. (2012). Epidemic intelligence: for the
crowd, by the crowd. In Proceedings of the 12th international
conference on Web Engineering, Berlin.
Tay, F.E.H. & Shen, L. (2002). Economic and financial prediction
using Rough sets model.European Journal of Operational Research,
(3):641–659.
Tripathy, B. K., Acharjya, D. P. & Cynthya, V. (2011). A
framework for intelligent medical diagnosis using rough set
with formal concept analysis. International Journal of Artificial
Intelligence & Applications, 2(2):1-14.
Twitter (2015). Twitter Developer Page (https://dev.twitter.com/
docs/api/1/get/search) [Accessed: 25-June-2015].
