Prediction of Clinical Pregnancy Occurrence After ICSI using Decision Tree and Support Vector Machine Methods

Background & Objective: Studies have shown that despite the numerous research carried out regarding infertility treatment, there is still a long way to treat this disease satisfactorily. Spending a lot of time and money on infertility treatments proves the necessity of designing a model which could predict the result of treatment methods with an acceptable accuracy; a model that could help physicians to get rid of trial and error for treatment methods which should step by step be applied on an infertile couple. Intracytoplasmic Sperm Injection (ICSI) is one of the assisted reproductive techniques. Statistics have indicated that the probability of pregnancy occurrence is only about 30% using this method. In this paper, a model which could predict the result of (ICSI) was presented using the decision tree and support vector machine methods. Materials & Methods: The applied data were collected in seven months from December 2012 to June 2013 by analyzing 251 treatment cycles in Omid Fertility Clinic. Input variables of the model were parameters like couple’s medical records, hormonal tests, the cause of infertility, and the like. The output variable was the occurrence or nonoccurrence of the clinical pregnancy (the pregnancy resulting in the formation of the fetal heart). One of the innovations of this study was that the input variables of the model were only preoperative, while in previous studies, having information about some of the surgery stages, such as quality of the egg and the like, was required to anticipate the result of the surgery. Results: The obtained accuracy using the decision tree and support vector machine methods were 70.3% and 75.7%, respectively. Conclusion: The results of the current study demonstrated that the support vector machine method had a better performance compared to the decision tree method. Presented model predicts the occurrence or nonoccurrence of a clinical pregnancy follows (ICSI), with a precision of 75.7%.

Keywords:

Infertility , Intracytoplasmic sperm injection , Decision tree , Support vector machine