A machine learning technology to improve the risk of non-invasive prenatal tests

Abstract

BACKGROUND: Timely and accurate diagnosis of genetic diseases can lead to proper action and prevention of irreparable events. OBJECTIVE: In this work we propose an integrated genetic-neural network (GNN) to improve the prediction risk of trisomy diseases including Down’s syndrome (T21), Edwards’ syndrome (T18) and Patau’s Syndrome (T13). METHODS: A dataset including 561 pregnant were created. In this integrated model, the structure and input parameters of the proposed multilayer feedforward network (MFN) were optimized. RESULTS: The results of execution of the GNN on the testing dataset showed that the developed model can be accurately classify the anomalies from healthy fetus with 97.58% accuracy rate, and 99.44% and 85.65% sensitivity, and specificity, respectively. In the proposed GNN model, the Levenberg Merquident (LM) algorithm, the Radial Basis (Radbas) function from various types of functions were selected by the proposed GA. Moreover, maternal age, Nuchal Translucency (NT), Crown-rump length (CRL), Pregnancy-associated plasma protein A (PAPP-A) were selected by the proposed GA as the most effective factors for classifying the healthfetuses from the cases with fetal disorders. CONCLUSION: The proposed computerized model increases the diagnostic performance of the physicians especially in the accurate detection of healthy fetus with non – invasive and low – cost treatments.