An Intelligent Prenatal Screening System for the Prediction of Trisomy-21

Abstract

Objective: Early and accurate diagnosis of genetic diseases such as Down syndrome can lead to correct action and prevent irreversible events. Therefore, the use of accurate and low-risk diagnostic methods to detect these diseases in the fetal period is important. In this study, a combined artificial neural network (ANN) and genetic algorithm (GA) was used to predict Down syndrome through first trimester screening test. In order to examine the proposed model, sample data were collected on 381 pregnant women who referred to a private screening reference laboratory for the first trimester screening test and NT ultrasound between 11 and 13 weeks of gestation. The proposed model in this study was a feedforward neural which its structure and input parameters were determined by (GA).Results: The average of 10 times experiments showed that the developed model can accurately identify cases of Down syndrome with specificity of 99.72% and sensitivity of 90.91%, and a mean square error (MSE) of 0.61%. The results of this study showed that the use of GA in optimizing the structure of the neural network technique can increase the accuracy in diagnosing Down syndrome through the information of first trimester screening tests.

Machine Learning Techniques to Enhance the Mental Age of Down Syndrome Individuals: A Detailed Review

“…Down syndrome is caused by the presence of an extra copy of chromosome-21. The prevalence level of DSI is approximately 1 in every 800 births [13]. These individuals may have significant cognitive impairments and have an intelligence quotient (IQ) ranging from 30 to 70 percent.…”