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Autonomous Adaptive Data Mining for u-Healthcare

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Rough Set and Knowledge Technology (RSKT 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6401))

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Abstract

Ubiquitous healthcare requires intelligence in order to be able to react to different patients needs. The context and resources con- straints of the ubiquitous devices demand a mechanism able to estimate the cost of the data mining algorithm providing the intelligence. The per- formance of the algorithm is independent of the semantics, this is to say, knowing the input of an algorithm the performance can be calculated. Under this assumption we present formalization of a mechanism able to estimate the cost of an algorithm in terms of efficacy and efficiency. Further, an instantiation of the mechanism for an application predicting glucose level for diabetic patients is presented.

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References

  1. Pappada, S.M., Cameron, B.D., Rosman, P.M.: Development of a neural network for prediction of glucose concentration in type 1 diabetes patients

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  2. Siewiorek, D., Smailagic, A., Furukawa, J., Krause, A., Moraveji, N., Reiger, K., Shaffer, J., Wong, F.L.: Sensay: A context-aware mobile phone. In: ISWC, Washington, DC, USA. IEEE Computer Society, Los Alamitos (2003)

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  3. Zanda, A., Eibe, S., Menasalvas, E.: Adapting batch learning algorithms execution in ubiquitous devices. In: MDM, Kansas city, USA. IEEE Computer Society, Los Alamitos (2010)

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Author information

Authors and Affiliations

  1. Facultad de Informatica, Universidad Politecnica Madrid, Spain

    Andrea Zanda, Santiago Eibe & Ernestina Menasalvas

Authors
  1. Andrea Zanda
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  2. Santiago Eibe
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  3. Ernestina Menasalvas
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Editor information

Editors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China

    Jian Yu

  2. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  3. Department of Mathematics and Computing Science, Saint Mary’s University, B3H 3C3, Halifax, Nova Scotia, Canada

    Pawan Lingras

  4. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Guoyin Wang

  5. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2010 Springer-Verlag Berlin Heidelberg

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Zanda, A., Eibe, S., Menasalvas, E. (2010). Autonomous Adaptive Data Mining for u-Healthcare. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_61

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  • DOI: https://doi.org/10.1007/978-3-642-16248-0_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16247-3

  • Online ISBN: 978-3-642-16248-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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