PREDICTING IMPLANTATION OUTCOME OF IN VITRO FERTILIZATION AND INTRACYTOPLASMIC SPERM INJECTION USING DATA MINING TECHNIQUES

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

HAFIZ PEGAH; NEMATOLLAHI MOHTARAM; BOOSTANI REZA; NAMAVAR JAHROMI BAHIA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Fertility & Sterility Year:2017 | Volume:11 | Issue:3 Page(s): 184-190

Download Full-Text

View:

350

Download:

140

Cites:

Information Journal Paper

Title

PREDICTING IMPLANTATION OUTCOME OF IN VITRO FERTILIZATION AND INTRACYTOPLASMIC SPERM INJECTION USING DATA MINING TECHNIQUES

Author(s)

HAFIZ PEGAH | NEMATOLLAHI MOHTARAM | BOOSTANI REZA | NAMAVAR JAHROMI BAHIA | Issue Writer Certificate

Keywords

IN VITRO FERTILIZATION

INTRACYTOPLASMIC SPERM INJECTION

CLINICAL DECISION SUPPORT

Abstract

Background: IN VITRO FERTILIZATION (IVF) and INTRACYTOPLASMIC SPERM INJECTION (ICSI) are two important subsets of the assisted reproductive techniques, used for the treatment of infertility. Predicting implantation outcome of IVF/ICSI or the chance of pregnancy is essential for infertile couples, since these treatments are complex and expensive with a low probability of conception.Materials and Methods: In this cross-sectional study, the data of 486 patients were collected using census method. The IVF/ICSI dataset contains 29 variables along with an identifier for each patient that is either negative or positive. Mean accuracy and mean area under the receiver operating characteristic (ROC) curve are calculated for the classifiers. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios of classifiers are employed as indicators of performance. The state-of-art classifiers which are candidates for this study include support vector machines, recursive partitioning (RPART), random forest (RF), adaptive boosting, and one-nearest neighbor.Results: RF and RPART outperform the other comparable methods. The results revealed the areas under the ROC curve (AUC) as 84.23 and 82.05%, respectively. The importance of IVF/ICSI features was extracted from the output of RPART. Our findings demonstrate that the probability of pregnancy is low for women aged above 38.Conclusion: Classifiers RF and RPART are better at predicting IVF/ICSI cases compared to other decision makers that were tested in our study. Elicited decision rules of RPART determine useful predictive features of IVF/ICSI. Out of 20 factors, the age of woman, number of developed embryos, and serum estradiol level on the day of human chorionic gonadotropin administration are the three best features for such prediction.

Cites

References

No record.

Cite

APA: Copy

HAFIZ, PEGAH, NEMATOLLAHI, MOHTARAM, BOOSTANI, REZA, & NAMAVAR JAHROMI, BAHIA. (2017). PREDICTING IMPLANTATION OUTCOME OF IN VITRO FERTILIZATION AND INTRACYTOPLASMIC SPERM INJECTION USING DATA MINING TECHNIQUES. INTERNATIONAL JOURNAL OF FERTILITY AND STERILITY, 11(3), 184-190. SID. https://sid.ir/paper/305937/en

Vancouver: Copy

HAFIZ PEGAH, NEMATOLLAHI MOHTARAM, BOOSTANI REZA, NAMAVAR JAHROMI BAHIA. PREDICTING IMPLANTATION OUTCOME OF IN VITRO FERTILIZATION AND INTRACYTOPLASMIC SPERM INJECTION USING DATA MINING TECHNIQUES. INTERNATIONAL JOURNAL OF FERTILITY AND STERILITY[Internet]. 2017;11(3):184-190. Available from: https://sid.ir/paper/305937/en

IEEE: Copy

PEGAH HAFIZ, MOHTARAM NEMATOLLAHI, REZA BOOSTANI, and BAHIA NAMAVAR JAHROMI, “PREDICTING IMPLANTATION OUTCOME OF IN VITRO FERTILIZATION AND INTRACYTOPLASMIC SPERM INJECTION USING DATA MINING TECHNIQUES,” INTERNATIONAL JOURNAL OF FERTILITY AND STERILITY, vol. 11, no. 3, pp. 184–190, 2017, [Online]. Available: https://sid.ir/paper/305937/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops