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Machine Learning-based Classifiers for the Prediction of Low Birth Weight
Mahya Arayeshgari1, Somayeh Najafi-Ghobadi2, Hosein Tarhsaz1
1Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Insights
Predicting low birth weight (LBW) is crucial for infant health. Logistic regression identified key factors like gestational age and maternal history, enabling targeted interventions to reduce LBW prevalence.
Area of Science:
- Medical Informatics
- Public Health
- Reproductive Health
Background:
- Low birth weight (LBW) is a significant global health issue with long-term consequences for child development and adult health.
- Factors contributing to LBW are diverse and region-specific, necessitating localized research and intervention strategies.
- Early identification and management of LBW risk factors are critical for improving neonatal outcomes.
Purpose of the Study:
- To compare the predictive performance of four machine learning classifiers for low birth weight (LBW).
- To identify the most significant factors associated with LBW in the Hamadan region of Iran.
- To inform public health strategies aimed at reducing LBW prevalence.
Main Methods:
- A retrospective cross-sectional study analyzed data from 741 mother-newborn pairs at Fatemieh Hospital in 2017.
- Five machine learning models, including logistic regression (LR), decision tree, random forest, and support vector machine, were employed for LBW prediction.
- Model performance was evaluated using five criteria, including accuracy, sensitivity, and specificity.
Main Results:
- The study found a 7% prevalence of low birth weight (LBW).
- All machine learning models achieved an average accuracy of 87% or higher in predicting LBW.
- Logistic regression (LR) demonstrated strong performance with 88% accuracy, identifying gestational age, number of abortions, gravida, consanguinity, maternal age, and neonatal sex as key predictors.
Conclusions:
- The findings highlight the effectiveness of logistic regression in predicting LBW and identifying critical associated factors.
- Interventions focusing on timely abortion diagnosis, genetic counseling for consanguineous couples, and enhanced prenatal care, especially for young mothers, are recommended.
- Strengthening preconception and prenatal care is essential for reducing the incidence of low birth weight (LBW).
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.
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