Advertisement

DengueViz: A Knowledge-Based Expert System Integrated with Parallel Coordinates Visualization in the Dengue Diagnosis

  • Jodene Yen Ling Ooi
  • J. Joshua ThomasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)

Abstract

The DengueViz is a knowledge-based expert system integrated with parallel coordinates as its visualization technique to diagnose dengue. The dengue diagnosis results includes the dengue classifications and their probability according to the interactions of users with the system. The knowledge base of this system consists of 140 rules for the classification of dengue. The integration of parallel coordinates visually presents the large amount of dengue information into a single visualization, where data interactions such as the selection of axes, filtering and highlighting reduces the clutter for it to be more comprehensible and enhances the correlation between the attributes of the information.

Keywords

Dengue Knowledge-based expert system Information visualization Parallel coordinates Visual data interactions Dengue classification Probability diagnosis 

Notes

Acknowledgements

KDU College (PG) Sdn Bhd has funded the work as an internal research grant scheme to the Department of Computing to conduct the computational medical research. We thank KDU Penang University College, Intelligent Processing Applications (IPA) research cluster under the Department of Computing has provided the venue to conduct and complete the research work.

References

  1. 1.
    World Health Organization (WHO). http://www.who.int
  2. 2.
  3. 3.
    Arbee, A.: Dengue cases to spike between June and August: Health Ministry. (2016). https://www.nst.com.my/news/2016/04/137488/dengue-cases-spike-between-june-and-august-health-ministry
  4. 4.
    World Health Organization (WHO): Dengue Haemorrhagic Fever: Diagnosis, Treatment, Prevention and Control. Geneva (1997)Google Scholar
  5. 5.
    Desai, M.N., Kartikeyn, B., Dahiya, V.: Applications of expert system in medical field. J. Expert Syst. 2, 150–152 (2015)Google Scholar
  6. 6.
    Darai, D.S., Singh, S., Biswas, S.: Knowledge Engineering-an overview. Int. J. Comput. Sci. Inf. Technol. 1, 230–234 (2010)Google Scholar
  7. 7.
    Sharma, T., Tiwari, N., Kelkar, D.: Study of difference between forward and backward reasoning. Int. J. Emerg. Technol. Adv. Eng. 2, 1–3 (2012)Google Scholar
  8. 8.
    Shortliffe, E.H.: Mycin: a knowledge-based computer program applied to infectious diseases. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, pp. 66–69. PubMed Central, California (1977)Google Scholar
  9. 9.
    Srikiatkhachorn, A., Rothman, A.L., Gibbons, R.V., Sittisombut, N., Malasit, P., Ennis, F.A., Nimmannitya, S., Kalayanarooj, S.: Dengue- how best to classify it. Clin. Infect. Dis. 53, 563–567 (2011)CrossRefGoogle Scholar
  10. 10.
    Watt, G., Jongsakul, K., Chouriyagune, C., Paris, R.: Differentiating dengue virus infection from scrub typhus in thai adults with fever. Am. J. Trop. Med. Hyg. 68, 536–538 (2003)CrossRefGoogle Scholar
  11. 11.
    Chang, K., Lu, P.-L., Ko, W.-C., Tsai, J.-J., Tsai, W.-H., Chen, C.-D., Chen, Y.-H., Chen, T.-C., Hsieh, H.-C., Pan, C.-Y., Harn, M.-R.: Dengue fever scoring system: new strategy for the early detection of acute dengue virus infection in Taiwan. J. Formos. Med. Assoc. 108, 879–885 (2009)CrossRefGoogle Scholar
  12. 12.
    Mitra, S., Gautam, I., Jambugulam, M., Abhilash, K.P., Jayaseeelan, V.: Clinical score to differentiate scrub typhus and dengue: a tool to differentiate scrub typhus and dengue. J. Glob. Infect. Dis. 9, 12 (2017)CrossRefGoogle Scholar
  13. 13.
    Xuan, C., Phuong, T., Nhan, N.T., Kneen, R.: Clinical diagnosis and assessment of severity of confirmed dengue infections in vietnamese children: is the world health organization classification system helpful? Am. J. Trop. Med. Hyg. 70, 172–179 (2004)Google Scholar
  14. 14.
    Pongpan, S., Wisitwong, A., Tawichasri, C., Patumanond, J., Namwongprom, S., Casimir, G.J., Tokiwa, K., Vasarhelyi, B.: Clinical Study Development of Dengue Infection Severity Score. ISRN Pediatr. 2013, 1–6 (2013). doi: 10.1155/2013/845876 CrossRefGoogle Scholar
  15. 15.
    Pone, S.M., Hökerberg, H.Y.M., De Oliveira, R. de C.V., Daumas, R.P., Pone, T.M., Pone, M.V.D.S., Brasil, P.: Clinical and laboratory signs associated to serious dengue disease in hospitalized children. J. Pediatr. (Rio J) 92, 464–471 (2016). doi: 10.1016/j.jped.2015.12.005
  16. 16.
    Chittaro, L.: Information visualization and its application to medicine. Artif. Intell. Med. 22, 81–88 (2001). doi: 10.1016/S0933-3657(00)00101-9 CrossRefGoogle Scholar
  17. 17.
    Thomas, J.J, Khader, A.T., Belaton, B.: A parallel coordinates visualization for the uncapaciated examination timetabling problem. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Shih, T.K., Velastin, S., Nyström, I. (eds.) IVIC 2011. LNCS, vol. 7066, pp. 87–98. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25191-7_10 CrossRefGoogle Scholar
  18. 18.
    Steinparz, S., Abmair, R., Bauer, A., Feiner, J.: InfoVis – parallel coordinates (2010). http://courses.iicm.tugraz.at/ivis/surveys/ss2010/g3-survey-parcoord.pdf
  19. 19.
    Riley, G.: CLIPS (2013). http://www.clipsrules.net
  20. 20.
    Kumar, S., Prasad, R.: Importance of Expert System Shell in Development of Expert System. Int. J. Innov. Res. Dev. 4, 128–133 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computing, School of Engineering, Computing and Built EnvironmentKDU Penang University CollegeGeorge TownMalaysia

Personalised recommendations