A NEURAL-NETWORK TO ANALYZE FERTILITY DATA

Objective: To program an artificial intelligence system, a neural network, and use it to predict results of sperm penetration in bovine cervical mucus (Penetrak assay; Serono Laboratories, Norwell, MA) and zona-free hamster egg penetration from the semen analysis. Design: Results of 139 Penetrak assays, 1,416 zona-free hamster egg penetration assays, and the corresponding semen analyses were retrospectively analyzed by an artificial neural network. Main Outcome Measures: Classification errors of the neural network were compared with those of linear and quadratic discriminant function analyses. Results: Data were separated into training and test sets. For the Penetrak result, linear and quadratic discriminant function analysis correctly predicted 58% and 74% of the training set results and only 64.1% and 69.2% of the test data, respectively. The neural network correctly predicted 92% of training set results and 80% of test set results. For the zona-free hamster egg penetration assay outcome, linear and quadratic discriminant function analysis correctly classified 66.3% and 46.0% of the training set and 64.9% and 44.7% of the test set, respectively. The neural network correctly classified 75.7% of the training data and 67.8% of the test data. Conclusions: Using the semen analysis, the neural network correctly classified 67.8% of zona-free hamster egg penetration assay results and 80% of Penetrak results it had not encountered previously, suggesting that this method of data analysis may be successfully employed to predict fertility potential.