چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | رضا فردوسی |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | دانشکده مدیریت و اطلاع رسانی پزشکی |
| کد مقاله | 66831 |
| عنوان فارسی مقاله | Rankinkg of Female Factor in Implantation Using DataMining |
| عنوان لاتین مقاله | Rankinkg of Female Factor in Implantation Using DataMining |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | The 4th International Congress on Reproduction |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | tehran |
| سال انتشار/ ارائه شمسی | 1397 |
| سال انتشار/ارائه میلادی | 2018 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1397/02/05 الی 1397/02/07 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | http://www.irciran.com/ |
| آدرس علمی (Affiliation) نویسنده متقاضی | Department of Health Information Technology, Tabriz University of Medical Sciences, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| بهناز رائف | اول |
| رضا فردوسی | دوم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Ranking of Female Factors in Implantation Using Data Mining Techniques Abstract Backgournd: In spite of improvements in infertility treatment, pregnancy rates have not increased significantly. Assisted reproductive technologies (ART) include costly and complexity processes. The aim of this study is determine the attributes and their particular values affecting the outcome in ART. Method: In this cross-sectional study, the data of 367 patients were collected using census method. The dataset contains 24 variables along with an identifier for each patient that is either negative or positive. To determine the significance of the female features, ranking-based algorithms such as Gain ratio and Gini Index run in Orange data mining software. Results: The results revealed the endometriosis is the importance factor among female pathologies. Our findings also demonstrate that the Infertility duration has highest score in Implantation outcomes. Conclusion: Elicited decision rules of ranking algorithms determine useful predictive features of Implantation. Out of 24 factors, the Infertility duration Thrombophilic disorders, and the status of period (Means) are the three best features for such prediction. |
| کلمات کلیدی | Clinical Decision Support, Data Mining, ranking algorithms, Assisted reproductive technologies (ART),Predictive factors |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| Rankinkg of Femail Factor in Implantation Using DataMining CONF.pdf | 1398/02/24 | 72957 | دانلود |
| Rankinkg of Femail Factor in Implantation Using DataMining CONF.pdf | 1398/02/24 | 72957 | دانلود |
