The aim of this study was to build an Artificial Neural Network (ANN) complemented by a decision tree to predict the chance of live birth after an In Vitro Fertilization (IVF)/Intracytoplasmic Sperm Injection (ICSI) treatment, before the first embryo transfer, using demographic and clinical data. Overall, 26 demographic and clinical data from 1193 cycles who underwent an IVF/ICSI treatment at Centro de Infertilidade e Reprodução Medicamente Assistida, between 2012 and 2019, were analyzed. An ANN was constructed by selecting experimentally the input variables which most correlated to the target through Pearson correlation. The final used variables were: woman’s age, total dose of gonadotropin, number of eggs, number of embryos and Antral Follicle Count (AFC). A decision tree was developed considering as an initial set the input variables integrated in the previous model. The ANN model was validated by the holdout method and the decision tree model by the 10-fold cross method. The ANN accuracy was 75.0% and the Area Under the Receiver Operating Characteristic (AUROC) curve was 75.2% (95% Confidence Interval (CI): 72.5–77.5%), whereas the decision tree model reached 75.0% and 74.9% (95% CI: 72.3–77.5%). These results demonstrated that both ANN and decision tree methods are fair for prediction the chance of conceive after an IVF/ICSI cycle.

Background: The prevalence of infertility ranges from 3.5% to 16.7% in more developed countries. For this reason, the number of In Vitro Fertilization(IVF)  technique and Intracytoplasmic Sperm Injection (ICSI) treatments has been significantly increasing. Several factors affect the success rate of in vitro treatments, which can be used to calculate the probability of success for each couple. As these treatments are complicated, expensive and with a variable probability of success, the most common question asked by IVF patients is “What are my chances of conceiving before starting an IVF/ICSI treatment?”. The main aim of this study is to develop a validated model that estimates the chance of a live birth before the start of an IVF/ICSI non-donor cycle. Methods: A logistic regression model was developed based on the retrospective study of 737 IVF/ICSI cycles. Overall 14 pre-treatment variables were evaluated (woman’s and man’s age, duration of infertility, cause of infertility, woman’s and man’s Body Mass Index (BMI), Anti-Müllerian Hormone (AMH), Antral Follicle Count (AFC), woman’s and man’s ethnicity, woman’s and man’s smoking status and woman’s and man’s previous live children) and the outcome of the treatment was discriminated as "live birth" or "no live birth". Results: From the 14 variables acquired before starting the IVF/ICSI procedures, only male factor, man’s BMI, man's mixed ethnicity and level of AMH were statistically significant. The interactions between infertility duration and woman’s age, infertility duration and man’s BMI, AFC and AMH, AFC and woman’s age, AFC and woman’s BMI, and AFC and disovulation were also statistically significant. The Area Under the Receiver Operating Characteristic (AUROC) curve test for the discriminatory ability of the final prediction model was 0.700 (95% Confidence Interval (CI) 0.660–0.741). Conclusions: This model might result in a new validated decision support system to help physicians to manage couples’ pre-treatment expectations.