NDLI: Machine learning-based approach for predicting low birth weight

Content Provider	Springer Nature : BioMed Central
Author	Ranjbar, Amene Montazeri, Farideh Farashah, Mohammadsadegh Vahidi Mehrnoush, Vahid Darsareh, Fatemeh Roozbeh, Nasibeh
Abstract	Background Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health risks. The current study employed a machine learning model to predict LBW. Methods This study implemented predictive LBW models based on the data obtained from the “Iranian Maternal and Neonatal Network (IMaN Net)” from January 2020 to January 2022. Women with singleton pregnancies above the gestational age of 24 weeks were included. Exclusion criteria included multiple pregnancies and fetal anomalies. A predictive model was built using eight statistical learning models (logistic regression, decision tree classification, random forest classification, deep learning feedforward, extreme gradient boost model, light gradient boost model, support vector machine, and permutation feature classification with k-nearest neighbors). Expert opinion and prior observational cohorts were used to select candidate LBW predictors for all models. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score were measured to evaluate their diagnostic performance. Results We found 1280 women with a recorded LBW out of 8853 deliveries, for a frequency of 14.5%. Deep learning (AUROC: 0.86), random forest classification (AUROC: 0.79), and extreme gradient boost classification (AUROC: 0.79) all have higher AUROC and perform better than others. When the other performance parameters of the models mentioned above with higher AUROC were compared, the extreme gradient boost model was the best model to predict LBW with an accuracy of 0.79, precision of 0.87, recall of 0.69, and F1 score of 0.77. According to the feature importance rank, gestational age and prior history of LBW were the top critical predictors. Conclusions Although this study found that the extreme gradient boost model performed well in predicting LBW, more research is needed to make a better conclusion on the performance of ML models in predicting LBW.
Related Links	https://bmcpregnancychildbirth.biomedcentral.com/counter/pdf/10.1186/s12884-023-06128-w.pdf
Ending Page	7
Page Count	7
Starting Page	1
File Format	HTM / HTML
ISSN	14712393
DOI	10.1186/s12884-023-06128-w
Journal	BMC Pregnancy and Childbirth
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-11-20
Access Restriction	Open
Subject Keyword	Reproductive Medicine Maternal and Child Health Gynecology Low birth weight Fetal weight Birth weight Machine learning X gradient boost model
Content Type	Text
Resource Type	Article
Subject	Obstetrics and Gynecology
Journal Impact Factor	2.8/2023
5-Year Journal Impact Factor	3.4/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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