Applications of logistic regression and artificial neural network for ICSI prediction

Abstract EN

The third most serious disease estimated by Word Wide Organization after cancer and cardiovascular disease is the infertility.

The advanced treatment techniques is the Intra-Cytoplasmic Sperm Injection (ICSI) procedure, it represents the best chance to have a baby for couples having an infertility problem.

ICSI treatment is expensive, and there are many factors affecting the success of the treatment, including male and female factors.

The paper aims to classify and predict the ICSI treatment results using logistic regression and artificial neural network.

For this purpose, data are extracted from real patients and contain parameters such as age, endometrial receptivity, endometrial and myometrial vascularity index, number of embryo transfer, day of transfer, and quality of embryo transferred.

Overall, the logistic regression predicts the output of the ICSI outcome with an accuracy of 75%.

In other parts, the neural network managed to achieve an accuracy of 79.5% with all parameters and 75% with only the significant parameters.