Machine Learning-based Classifiers for the Prediction of Low Birth Weight
Objectives Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The...
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2023-01-01
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| Series: | Healthcare Informatics Research |
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| Online Access: | http://www.e-hir.org/upload/pdf/hir-2023-29-1-54.pdf |
| _version_ | 1797905394343870464 |
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| author | Mahya Arayeshgari Somayeh Najafi-Ghobadi Hosein Tarhsaz Sharareh Parami Leili Tapak |
| author_facet | Mahya Arayeshgari Somayeh Najafi-Ghobadi Hosein Tarhsaz Sharareh Parami Leili Tapak |
| author_sort | Mahya Arayeshgari |
| collection | DOAJ |
| description | Objectives Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran. Methods We carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance. Results Our findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW. Conclusions Our findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW. |
| first_indexed | 2024-04-10T10:04:34Z |
| format | Article |
| id | doaj.art-6ab8d2f2727341ff98ab64d1ad495591 |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-04-10T10:04:34Z |
| publishDate | 2023-01-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-6ab8d2f2727341ff98ab64d1ad4955912023-02-16T01:30:29ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2023-01-01291546310.4258/hir.2023.29.1.541148Machine Learning-based Classifiers for the Prediction of Low Birth WeightMahya Arayeshgari0Somayeh Najafi-Ghobadi1Hosein Tarhsaz2Sharareh Parami3Leili Tapak4 Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran Department of Industrial Engineering, Faculty of Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, IranObjectives Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran. Methods We carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance. Results Our findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW. Conclusions Our findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW.http://www.e-hir.org/upload/pdf/hir-2023-29-1-54.pdfinfantlow birth weightgestational ageabortioninducedlogistic modelsmachine learning |
| spellingShingle | Mahya Arayeshgari Somayeh Najafi-Ghobadi Hosein Tarhsaz Sharareh Parami Leili Tapak Machine Learning-based Classifiers for the Prediction of Low Birth Weight Healthcare Informatics Research infant low birth weight gestational age abortion induced logistic models machine learning |
| title | Machine Learning-based Classifiers for the Prediction of Low Birth Weight |
| title_full | Machine Learning-based Classifiers for the Prediction of Low Birth Weight |
| title_fullStr | Machine Learning-based Classifiers for the Prediction of Low Birth Weight |
| title_full_unstemmed | Machine Learning-based Classifiers for the Prediction of Low Birth Weight |
| title_short | Machine Learning-based Classifiers for the Prediction of Low Birth Weight |
| title_sort | machine learning based classifiers for the prediction of low birth weight |
| topic | infant low birth weight gestational age abortion induced logistic models machine learning |
| url | http://www.e-hir.org/upload/pdf/hir-2023-29-1-54.pdf |
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