Data Mining in Infertility Problems

• Main Authors: Wei-Hau Whiu, 許韋皓
• Other Authors: Ping-Feng Pai
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/40564226950092605314

碩士 === 國立暨南國際大學 === 管理學院經營管理碩士學位學程碩士在職專班 === 97 === Title of Thesis： Data Mining in Infertility Problems Name of Institute：National Chi Nan University Pages：76 Graduation Time：July/2009 Degree Conferred：Master Student Name：Wei-Hau Shiu Advisor Name：Dr. Ping-Feng Pai Abstract In Taiwan, factors such as sex liberation, legalization of artificial abortion, late marriage and work stress have caused infertility in one out of every seven to ten couples. Infertility is a disease of the human reproductive system; it is defined as the incapability in achieving pregnancy in spite of determined attempts by heterosexual intercourse without contraception within a one year period. This research employs the artificial reproduction database from the Bureau of Health Promotion (Department of Health, R.O.C.) and explores the artificial reproduction data of the general public. In addition, a set of data prediction model for artificial reproduction is established and the significant factors of infertility in the assisted reproduction database are investigated. This system is expected to provide the physicians with preoperative analysis and decision for surgical procedures in assisted reproduction, which would improve the conception rate. Previously, obstetricians and gynecologists generally make decisions based on their previous experience when diagnosing infertile patients. This research adopts methods including doctor’s rule of thumb, Discriminant Analysis and Logistic Regression Analysis to select the important attributes from the data for doctor’s rule of thumb. Subsequently, the results for the categorization are compared through Rough Set Theory (RST), Discriminant Analysis (DA) and etc. After the experimental analysis, the accuracy rate for pregnancy prediction, reproduction prediction and infant weight prediction are above ninety percent. Moreover, this research is expected to provide reference for physicians when they are making decision on related diagnosis for infertile patients. Keywords: Data Mining, Discriminant Analysis(DA), Logistic Regression Analysis, Sequential Forward Selection(SFS), Factor Analysis(FA), Rough Set Theory (RST), Back-propagation Neural Network, Artificial Assisted Reproduction