Infant Low Birth Weight Prediction Using Graph Embedding Features

Wasif Khan, Nazar Zaki (), Amir Ahmad, Jiang Bian, Luqman Ali, Mohammad Mehedy Masud, Nadirah Ghenimi and Luai A. Ahmed
Additional contact information
Wasif Khan: Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Nazar Zaki: Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Amir Ahmad: Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Jiang Bian: Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, USA
Luqman Ali: Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Mohammad Mehedy Masud: Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Nadirah Ghenimi: Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Luai A. Ahmed: Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates

IJERPH, 2023, vol. 20, issue 2, 1-15

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.

Keywords: low birth weight; knowledge graph; topological features; healthcare; birth weight prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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