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An intelligent approach to discovering common symptoms among depressed patients

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Abstract

On world’s health care radar, one of the emerging fatal diseases is depression. Mainly young generation is becoming victim to this because of the fast pace of life. Extensive measures should be taken to overcome this trauma. Data are collected worldwide to gain some useful knowledge, but problem occurs in handling the large amount of data. Therefore, data mining techniques are being used to resolve the problems. In this paper, we have applied the data mining techniques such as association analysis and frequent pattern tree on depression database containing 5,964 records. These techniques are used altogether to extract efficient results. It saves the processing time and effort when used together. The results from our analysis state the most common symptoms of depressed patients as well as discuss the scenarios of the patients. The limitations of the suggested techniques help make an inference that how fuzzy concept is more beneficial in the given situation.

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Acknowledgments

This work was supported in part by the Ministry of Science and Technology, Taiwan, under Grants NSC NSC102-2221-E-027-083- and NSC102-2218-E-002-009-MY2 and in part by the joint project between the National Taipei University of Technology and Mackay Memorial Hospital under Grant NTUT-MMH-103-01.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taipei University of Technology, Taipei,  10608, Taiwan

    Yusra Ghafoor & Yo-Ping Huang

  2. Department of Psychiatry, Mackay Memorial Hospital, Taipei,  10449, Taiwan

    Shen-Ing Liu

Authors
  1. Yusra Ghafoor
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  2. Yo-Ping Huang
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  3. Shen-Ing Liu
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Corresponding author

Correspondence to Yo-Ping Huang.

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Communicated by Y.-P. Huang.

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Ghafoor, Y., Huang, YP. & Liu, SI. An intelligent approach to discovering common symptoms among depressed patients. Soft Comput 19, 819–827 (2015). https://doi.org/10.1007/s00500-014-1408-4

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  • DOI: https://doi.org/10.1007/s00500-014-1408-4

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