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Soft Computing

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01.04.2015 | Focus

An intelligent approach to discovering common symptoms among depressed patients

verfasst von: Yusra Ghafoor, Yo-Ping Huang, Shen-Ing Liu

Erschienen in: Soft Computing | Ausgabe 4/2015

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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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Titel: An intelligent approach to discovering common symptoms among depressed patients
verfasst von: Yusra Ghafoor
Yo-Ping Huang
Shen-Ing Liu
Publikationsdatum: 01.04.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1408-4

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