An intelligent approach to discovering common symptoms among depressed patients

作者：Ghafoor Yusra; Huang Yo Ping^*; Liu Shen Ing

来源：Soft Computing, 2015, 19(4): 819-827.

DOI：10.1007/s00500-014-1408-4

• Depression
• Data mining
• Association rules
• FP tree

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.

• 出版日期2015-4