A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm

• Published in: Diagnostics (Basel) Vol. 12; no. 11; p. 2643
• Main Authors: Verma, Deepti, Agrawal, Shweta, Iwendi, Celestine, Sharma, Bhisham, Bhatia, Surbhi, Basheer, Shakila
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 31.10.2022; MDPI
• Subjects: Adaptive Stochastic Gradient Descent Algorithm (ASGDA); Algorithms; Autopsies; Babies; Biometrics; Bones; Chromosomes; Diagnosis; Embryos; Families & family life; fetal abnormality; Fetal diseases; Fetuses; Health aspects; Health risk assessment; Heart; Identification; Neural networks; NT (nuchal translucency); Pregnancy; Prenatal diagnosis; Risk factors; risk score evaluation; Technology application; Ultrasonic imaging; Womens health; India
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12112643

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 related terms. Fetal abnormalities have been commonly observed in 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 such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.