Citation

Nagarajaiah, Kavyashree and Ooi, Chee Pun and Tan, Wooi Haw and Mohod, Swapnil B and Ingole, Ketki R and Hegde, Rajalaxmi (2026) Ensemble Forest-Boosted Classification Framework For Machine Learning-Based Detection Of Fetal Anomalies From Nuchal Translucency Data. Journal of Intelligent Decision Making and Information Science, 3 (7s). pp. 2066-2080. ISSN 3079-0875

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Abstract

In most maternity institutions, an ultrasound scan in the mid-trimester is now considered standard antenatal care. As technology advances and scanning capabilities improve, the discovery of fetal anomalies in scans increases. Foetal anomalies are developmental defects that originate in a foetus during pregnancy. Birth defects and congenital abnormalities are concepts that are linked. have consistently reported cases of foetal abnormalities. Three out of every 1,000 pregnant women get a fetal abnormality. This work presents an ensemble forest-boosted method for assessing the risk of fetal anomalies by analyzing nuchal translucency (NT). The data was initially retrieved from here. Following that, it can be processed using the colab mean filter. The region of interest can then be delimited using the iterative coarse-grained seed algorithm (ICGSA). The ensemble forest boosted classifier (EFBC) was finally used to detect fetal abnormalities. The findings of this study show that the proposed unique method can accurately classify the anomalies related with nuchal translucency thickening. The analysis includes characteristics including accuracy, recall, precision, and F1-score. The proposed approach achieves an accuracy of 99.642%.

Item Type:	Article
Uncontrolled Keywords:	Fetal abnormality, iterative coarse-grained seed algorithm,
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Sep 2026 07:40
Last Modified:	03 Sep 2026 07:40
URII:	http://shdl.mmu.edu.my/id/eprint/16640

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