caa a22 4500 60546992X CHVBK 20210128100325.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00500-014-1408-4 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1408-4 An intelligent approach to discovering common symptoms among depressed patients [Elektronische Daten] [Yusra Ghafoor, Yo-Ping Huang, Shen-Ing Liu] 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. Springer-Verlag Berlin Heidelberg, 2014 Depression nationallicence Data mining nationallicence Association rules nationallicence FP tree nationallicence Ghafoor Yusra Department of Electrical Engineering, National Taipei University of Technology, 10608, Taipei, Taiwan aut Huang Yo-Ping Department of Electrical Engineering, National Taipei University of Technology, 10608, Taipei, Taiwan aut Liu Shen-Ing Department of Psychiatry, Mackay Memorial Hospital, 10449, Taipei, Taiwan aut Soft Computing Springer Berlin Heidelberg 19/4(2015-04-01), 819-827 1432-7643 19:4<819 2015 19 500 https://doi.org/10.1007/s00500-014-1408-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1408-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ghafoor Yusra Department of Electrical Engineering, National Taipei University of Technology, 10608, Taipei, Taiwan aut NATIONALLICENCE 700 1- Huang Yo-Ping Department of Electrical Engineering, National Taipei University of Technology, 10608, Taipei, Taiwan aut NATIONALLICENCE 700 1- Liu Shen-Ing Department of Psychiatry, Mackay Memorial Hospital, 10449, Taipei, Taiwan aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/4(2015-04-01), 819-827 1432-7643 19:4<819 2015 19 500