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A novel framework for abnormal risk classification over fetal nuchal translucency using Adaptive Stochastic Gradient Descent Algorithm
Journal article   Open access   Peer reviewed

A novel framework for abnormal risk classification over fetal nuchal translucency using Adaptive Stochastic Gradient Descent Algorithm

Deepti Verma, Shweta Agrawal, Imed Ben Dhaou and Celestine Iwendi
Diagnostics, Vol.12(11), 2643
31/10/2022
PMID: 36359487

Abstract

NT (Nuchal Translucency) fetal abnormality Adaptive Stochastic Gradient Descent Algorithm (ASGDA) risk score 20evaluation.
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy. Birth defects and congenital abnormalities are certain fetal abnormalities. Fetal abnormalities have become common in several industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters like accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through suggested technique is of 98.642.%.
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