Kumar Ravi, Bahuguna Abhinav, Goyal Palak, Mishra Richa, Khan Huma, Kumar Amit
Department of Community Medicine, Shri Ram Murti Smarak Institute of Medical Sciences, Bareilly, India.
Department of Biostatistics, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, Uttarakhand, India.
Indian J Community Med. 2025 Aug;50(Suppl 1):S134-S139. doi: 10.4103/ijcm.ijcm_247_24. Epub 2025 Feb 27.
Birth weight plays a vital role in an infant's comprehensive development. Low birth weight (LBW) infants may go through several kinds of health complications in the early stages of their lives. This paper is an attempt to identify the predictors that significantly influence the likelihood of LBW through a model-based approach.
Data for this hospital based cross sectional study includes 130 pregnant women during the years 2022-2023. We have applied logistic regression and the decision tree method for predicting LBW in pregnancies. The performance of these predictive models has been assessed through receiving operating characteristic curve (ROC).
The findings revealed 38.5% prevalence of LBW in pregnancies. Factors such as age of mother, abortion, presence of co-morbidities, pregnancy complications, and gestational age have been identified as significant predictors ( < 0.05) of LBW through logistic regression. The area under the ROC curve (AUC=0.881) for logistic regression and decision tree (AUC=0.814) indicates that the fitted models have better discrimination ability.
Logistic have better accuracy than decision tree model. Decision tree excels at capturing patterns but may overfit and hence should be used with caution. This study highlighted the need of targeted policy implementation on maternal and childhood care to reduce the risk of LBW.
出生体重在婴儿的全面发育中起着至关重要的作用。低出生体重（LBW）婴儿在生命早期可能会经历多种健康并发症。本文试图通过基于模型的方法确定显著影响低出生体重可能性的预测因素。
这项基于医院的横断面研究的数据包括2022年至2023年期间的130名孕妇。我们应用逻辑回归和决策树方法来预测妊娠中的低出生体重情况。这些预测模型的性能已通过接受操作特征曲线（ROC）进行评估。
研究结果显示，妊娠中低出生体重的患病率为38.5%。通过逻辑回归，已确定母亲年龄、流产、合并症的存在、妊娠并发症和孕周等因素是低出生体重的显著预测因素（<0.05）。逻辑回归的ROC曲线下面积（AUC=0.881）和决策树的ROC曲线下面积（AUC=0.814）表明拟合模型具有更好的区分能力。
逻辑回归比决策树模型具有更高的准确性。决策树擅长捕捉模式，但可能会过度拟合，因此应谨慎使用。本研究强调了实施针对性的母婴护理政策以降低低出生体重风险的必要性。
