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Application of big data analysis with decision tree for the foot disorder

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Abstract

In medical field, massive data sets is generated by rapid development of hospital information system. For analysis of these medical big data, this study showed analysis process of the clinical data to acquire significant information effectively between the foot disorder groups and biomechanical parameters related to symptom by developing a prediction model of the decision tree. The first clinical health records of 1523 patients diagnosed with foot disorder were used for analysis, in total 6610 records. The dependent variable in the analysis data was consisted of five complex disorder groups, and the independent variable was composed of 24 attributes. The decision tree was applied to analyze pattern of the foot disorder. The measured prediction rate was Correct: 72.96 % and Wrong: 27.04 % in the training data, and Correct: 68.66 % and Wrong: 31.34 % in the test data. As a result of analysis on the five foot complex foot disorder groups by using C5.0 algorithm, 12 rules were generated. To improve accuracy of classification, the detailed preprocessing and other data mining algorithms will be applied from now on.

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Acknowledgments

This work was supported by National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (NRF-2013R1A2A2A04016782).

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Authors and Affiliations

  1. Department of Healthcare Engineering, Chonbuk National University, 664-14, Deokjin-dong 1, Deokjin-ku, Jeonju, Jeollabuk-do, 561-756, Korea

    Jung-Kyu Choi

  2. Department of Occupational therapy, Kunjang College, 608-8, Doamri, Sungsan-myun, Gunsan, Jeollabuk-do, 573-709, Korea

    Keun-Hwan Jeon

  3. School of Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 500-757, Korea

    Yonggwan Won

  4. Division of Biomedical Engineering, Research Center of Healthcare & Welfare Instrument for the Aged, Chonbuk National University, 664-14, Deokjin-dong 1, Deokjin-ku, Jeonju, Jeollabuk-do, 561-756, Korea

    Jung-Ja Kim

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  1. Jung-Kyu Choi
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Correspondence to Jung-Ja Kim.

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Choi, JK., Jeon, KH., Won, Y. et al. Application of big data analysis with decision tree for the foot disorder. Cluster Comput 18, 1399–1404 (2015). https://doi.org/10.1007/s10586-015-0480-6

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  • DOI: https://doi.org/10.1007/s10586-015-0480-6

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