Predictive Machine Learning Model for Low Birth Weight in Newborns
DOI:
https://doi.org/10.69660/jcsda.01022401Keywords:
Feature engineering;, Hyperparameter tuning, Low birth weight, Machine Learning, Neural Networks, Prediction model, Support Vector MachineAbstract
Low birth weight (LBW) occurs when a newborn weighs less than 2500 grams regardless of the gestational age of the infant. LBW is one of the indicators of complex public health problems of an infant, which is 25 times more likely to die than those at expected birth weight. The neonatal mortality rate measures the number of neonates dying before reaching 28 days of age per 1,000 live births within a given year. It affects one out of every seven newborns, about 14.6 percent of babies born worldwide. The prevalence is 7.2% in developed regions and 13.7% in Africa. The neonatal mortality rate in Ethiopia is 29.524 deaths from 1000 live births in 2023. Thus, building accurate LBW prediction models and finding the related risk factors is critical. The early identification and prediction of such disease would reduce premature death rate caused by LBW. The exponentially increasing data availability on the web has a significant role in extracting better insight, and it is vital to develop machine learning models. The current technology facilitates and supports professionals in health care by providing services to societies. Classification techniques help to classify the case according to a certain feature from data and to predict the probabilities of LBW in infants. We have used a public dataset obtained from the Ethiopia Demographic Health Survey to build models. In this work, artificial neural networks and support vector machines are used for training and model building. Moreover, data preparation techniques like scaling and transformation, hyperparameter tuning approaches like GridSearch CV, and feature engineering techniques have been employed and tested. Based on performance evaluation results, the proposed classifiers were capable of predicting of LBW. The neural network with hyperparameter tuning techniques scored an accuracy of over 97.2% with 98.0% of sensitivity, 97.0% of precision, 96.0% of specificity, and an area under the curve (AUC) of 99%.
