Title of article :
Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method
Author/Authors :
Mostaar, A Department of Medical Physics and Biomedical Engineering - School of Medicine - Shahid Beheshti University of Medical Sciences, Tehran, Iran , Sattari, M. R Department of Medical Physics and Biomedical Engineering - School of Medicine - Shahid Beheshti University of Medical Sciences, Tehran, Iran , Hosseini, S Preventive Gynecology Research Center - Shahid Beheshti University of Medical Sciences, Tehran, Iran , Deevband, M. R Department of Medical Physics and Biomedical Engineering - School of Medicine - Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract :
Background: Intracytoplasmic sperm injection (ICSI) or microinjection is one of
the most commonly used assisted reproductive technologies (ART) in the treatment of
patients with infertility problems. At each stage of this treatment cycle, many dependent
and independent variables may affect the results, according to which, estimating
the accuracy of fertility rate for physicians will be difficult.
Objective: This study aims to evaluate the efficiency of artificial neural networks
(ANN) and principal component analysis (PCA) to predict results of infertility treatment
in the ICSI method.
Material and Methods: In the present research that is an analytical study,
multilayer perceptron (MLP) artificial neural networks were designed and evaluated
to predict results of infertility treatment using the ICSI method. In addition, the PCA
method was used before the process of training the neural network for extracting information
from data and improving the efficiency of generated models. The network has
11 to 17 inputs and 2 outputs.
Results: The area under ROC curve (AUC) values were derived from modeling the
results of the ICSI technique for the test data and the total data. The AUC for total data
vary from 0.7670 to 0.9796 for two neurons, 0.9394 to 0.9990 for three neurons and
0.9540 to 0.9906 for four neurons in hidden layers.
Conclusion: The proposed MLP neural network can model the specialist performance
in predicting treatment results with a high degree of accuracy and reliability.
Keywords :
Intracytoplasmic Sperm Injection , Fertility , Principal Component Analysis , Neural Networks
Journal title :
Journal of Biomedical Physics and Engineering