Abstract
There are many problems with applying the machine learning technique, which is widely used in the conventional healthcare field, during the mobile u-health service analysis step. First, research on the mobile u-health service is just beginning, and there are very few cases where the existing techniques have been applied in the mobile u-health service environment. Second, since the machine learning technique requires a long learning period, it is not suitable for application in the mobile u-health service environment, which requires real-time disease management. Third, the various machine learning techniques that have been proposed until now do not include a way to assign the weight factors to the disease-related variables, and thus its use as a personalized disease prediction system is somewhat limited. This paper proposes PCADP, which is an ontology-based personalized disease prediction method, to solve such problems and to interpret the bio data analysis of the mobile u-health service system as a process. Moreover, the mobile u-health service ontology framework was modeled as a semantics type in order to meaningfully express the mobile u-health data and service statement based on PCADP. To validate the performance and efficiency of the PCADP technique proposed in this paper, the 5-cross validation method was used to measure the accuracy of the prediction. The validation of PCADP using a virtual disease group verified that the technique proposed in this paper shows much greater accuracy compared to existing methods. Moreover, the PCADP prediction method improved the flexibility and real-time attributes, which are the essential elements of any diagnosis technique in the mobile u-health environment, and showed efficiency in the continuous improvement of the monitoring and system of the diagnosis process.
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References
Kwon, J.-D.: Customer Characteristics Analysis of the Curing Expert System for Dementia or Other Disabilities. AlphaInternet Co. Ltd. (2001)
Han, D.-S., Ko, I.-Y., Park, S.-J.: A Study on the Development of the Mobile U-Health Service System, the final report of research with the same title from ICU, Korea (2006)
Han, D.-S., Ko, I.-Y., Park, S.-J.: Evolving Mobile U-Health Service Platform. In: Proceedings of Information Security Society, vol. 17(1), pp. 11–21 (2007)
Konstantas, D., Bults, R., Van Halteren, A., Wac, K., Jones, V., Wkdya, I., Herzog, R., Streimelweger, B.: Mobile Health Care: Towards a commercialization of research results. In: Proceedings of the 1st European Conference on eHealth-ECEH 2006, Fribourg, Switzerland, pp. 12–13 (October 2006)
Pappas, M., Coscia, C., Dodero, E., Gianuzzi, G., Earney, V.: A Mobile E-Health System Based on Workflow Automation Tools. In: Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems, pp. 271–276 (June 2002)
Min, B.-W., Oh, Y.-S., Han, D.-S., Ku, J.-Y.: Design of a Mobile U-Health Service Platform. In: Proceedings of the Fall 2009 Integrated Conference, vol. 7(1), pp. 797–801. Korea Contents Association (2009)
Lee, H.-S., Bak, J.-H., Sim, B.-K., Lee, H.-O., Han, S.-W., Min, B.-W., Lee, H.-T.: Web-based Patient Monitoring System Using a Wireless Diaper Wetness Sensor. In: Proceedings of ICCC 2008, vol. 6(2), pp. 652–660. Korea Contents Association (2008)
Min, B.-W., Lee, H.-T., Oh, Y.-S.: USN-Based Intelligent Urine Sensing U-Care System. In: Proceedings of the Spring 2008 Integrated Conference, vol. 5(2), pp. 598–601. Korea Contents Association (2008)
Min, B.-W., Oh, Y.-S.: Design of a U-Healthcare Product Using Wetness Sensor. In: Proceedings of the Spring 2007 Integrated Conference, vol. 3(2), pp. 144–147. Korea Contents Association (2007)
Park, H.-G., Kim, H.-J., Lee, S.-J.: A Transmission Management System for Signals from Living Bodies Using ZigBee. In: Proceedings of the 2008 Conference, vol. 32(1), pp. 526–528. Korea Computer Society (2005)
Klopotek, M.A.: A New Bayesian Tree Learning Method with Reduced Time and Space Complexity. Fundamenta Informaticae 49(4) (2002)
Inza, I., Merino, M., Larranage, P., Quiroga, J., Sierra, B., Girala, M.: Feature Subset by genetic algorithms and estimation of distribution algorithms, A case study in the survival of cirrhotic patients treated with TIPS. Artificial Intelligence in Medicine 23 (2001)
Cooper, C.F., et al.: An evaluation of machine learning methods for predicting pneumonia mortality. Artificial Intelligence in Medicine 9 (1997)
Fine, M.J., Hanusa, B.H., Lave, J.R., Singer, D.E., Stone, R.A., Weissfeld, L.A., Coley, C.M., Marrie, T.J., Kapoor, W.N.: Comparison of severity of illness measured in patients with community-acquired pneumonia. J. Gen. Int. Med. (1995)
Kim, D.H., Uhmm, S., Cho, S.W., Hahm, K.B., Kim, J.: A Predictive Model for Chronic Hepatitis Susceptibility from Single Nucleotide Polymorphism. In: BIOINFO 2006, pp. 35–38 (2006)
Cooper, C.F., et al.: An evaluation of machine learning methods for predicting pneumonia mortality. Artificial Intelligence in Medicine 9 (1997)
Tolentino, R.S., Park, S.: A Study on U-Healthcare System for Patient Information Management over Ubiquitous Medical Sensor Networks. IJAST 18, 1–10 (2010)
Mateo, R.M.A., Lee, J., Gerardo, B.D.: Healthcare Expert System based on Group Cooperation Model. IJSEIA 2(1), 105–116 (2008)
Cagalaban, G., Soh, W., Kim, S.: Devising an Optimal Scheme for Wireless Sensors for Patient Report Tracking and Monitoring in Ubiquitous Healthcare. IJSEIA 5(4), 63–76 (2011)
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Min, BW. (2012). Mobile U-Health Service System for Personalized Diagnosis Based on Ontology. In: Lee, G., Howard, D., Ślęzak, D., Hong, Y.S. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Communications in Computer and Information Science, vol 310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32692-9_31
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DOI: https://doi.org/10.1007/978-3-642-32692-9_31
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