Abstract
Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings.
Funder
Zayed Center for Health Sciences, United Arab Emirates University
Subject
Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health
Reference52 articles.
1. (2020, December 21). WHO|World Health Organization, Available online: https://www.who.int/.
2. Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms;Khan;Sci. Rep.,2022
3. Low birth weight leads to obesity, diabetes and increased leptin levels in adults: The CoLaus study;Jornayvaz;Cardiovasc. Diabetol.,2016
4. (2021, January 11). Reduction of Low Birth Weight: A South Asia Priority—PDF Free Download. Available online: https://docplayer.net/20755175-Reduction-of-low-birth-weight-a-south-asia-priority.html.
5. (2022, August 06). Sitecontrol Low Birthweight UNICEF DATA. Available online: https://data.unicef.org/topic/nutrition/low-birthweight/.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献