Infertility Diagnosis by Data Mining Techniques

• Main Authors: Salbi Heydari, Abolfazl Saeidifar
• Format: Article
• Language: fas
• Published: Qom University of Medical Sciences 2015-05-01
• Series: Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Qum
• Subjects: infertility; data mining; algorithms k-means; decision trees
• Online Access: http://journal.muq.ac.ir/article-1-379-en.html

Background and Objectives: According to wide mass data collection at medical centers and proper use of it in order to diagnosis of a malady needs to relevant tools and medical science for data analyzing. Infertility diagnosis studied by data mining techniques. Methods: All information had been extract from patientchr('39')s documents of ACECR Center for Infertility Treatment, Qom Branch; 700 sample were selected among 14,242 cases in 15 years of age, duration of infertility, family connections, infertility, family, job, male, female menstrual cycle type, hirsutism, galactorrhea, amenorrhea, cause of infertility, female body mass index, smoking and semen tests were used. The prediction algorithms C5.0, C & R tree, CHAID and K-means clustering algorithm to determine the optimal number of clusters Davis - Buldian used. Results: According to the accepted model, the error is less CHAID algorithm, the most important factor in infertility in the female body mass index, age, disease, hirsutism, infertility, family, illness, galactorrhea, the amount of sperm per milliliter, duration of infertility, old man, were consanguineous couples. According to this model, most of the menchr('39')s wear agents were identified. Conclusion: In this study, the effect of female infertility factors predicted.