An Intelligent Model for Prediction of In-Vitro Fertilization Success using MLP Neural Network and GA Optimization

In-Vitro Fertilization (IVF) is one of the scientifically known methods of infertility treatment. This work aims at improving the performance of predicting the success of IVF using machine learning and its optimization through evolutionary algorithms. The Multi-layer Perceptron (MLP) neural network is proposed in order to classify the infertility dataset. The Genetic algorithm (GA) is used to improve the performance of the MLP model. The proposed model is applied to a dataset including 594 eggs from 94 patients undergoing IVF, of which 318 are of good quality embryos and 276 are of lower quality embryos. For performance evaluation of the MLP model, an ROC curve analysis is conducted, and a 10-fold cross-validation is performed. The results reveal that this intelligent model has a high efficiency with an accuracy of 96% for MLP, which is promising compared to the counterpart methods.