Verma Indu, Prasad Sanjeev Kumar
School of Computer Science and Engineering, Galgotias University, Greater Noida, India.
Birth Defects Res. 2025 Feb;117(2):e2443. doi: 10.1002/bdr2.2443.
Infant mortality remains a critical public health issue, reflecting the overall health and well-being of a population. Accurate prediction of infant mortality is crucial, as it enables healthcare providers to identify at-risk populations and implement targeted interventions. By analyzing factors such as maternal education, prenatal care access, nutrition, and environmental influences, predictions help in designing effective programs aimed at reducing infant deaths.
This research paper aims to predict infant mortality in India by employing ensemble learning techniques, specifically eXtreme gradient boosting (XGBoost), stacking, adaptive boosting, and bagging. The data for the analysis are sourced from national surveys and demographic studies focusing on infant mortality in India. The collected data underwent rigorous preprocessing steps to prepare it for predictive modeling. Each ensemble learning model is applied to predict infant mortality rates based on the preprocessed data. The XGBoost handles complex and non-linear relationships within the data, and the stacking model is used for the accurate and robust predictions. The adaptive boosting model iteratively trains multiple weak learners, which makes the predictive model as stronger. The adaptive boosting technique enhances the performance of weak classifiers while effectively addressing class imbalance issues. Further, the bagging approach is implemented to derive the linear and non-linear relationships of infant mortality. Models were optimized using k-fold cross-validation to fine-tune their hyper parameters. The predictive ability of the ensemble techniques is analyzed by deploying using different performance parameters.
XGBoost attained superior performance results, with a 98.75% accuracy, 98.56% precision, and 98.24% recall. The adaptive boosting model strengthened weak learners and addressed class imbalance issues, while the bagging method captures linear and non-linear relationships. Ensemble learning models demonstrated effectiveness in predicting infant mortality, with XGBoost excelling in handling complex and non-linear relationships.
The simulation results revealed that ensemble learning models are highly effective in predicting infant mortality rates in India, with significant regional disparities observed. For example, the Northeast region exhibited the highest predicted infant mortality rates, while the South region recorded the lowest. These findings underscore the need for targeted interventions in high-mortality areas to reduce disparities. The study highlights the efficacy of ensemble learning models, particularly XGBoost, in predicting infant mortality in India. The findings emphasize the critical role of improving maternal education, access to prenatal care, and reducing socioeconomic disparities.
婴儿死亡率仍然是一个关键的公共卫生问题，反映了一个地区的整体健康状况。准确预测婴儿死亡率至关重要，因为这能使医疗服务提供者识别出高危人群并实施针对性干预措施。通过分析诸如母亲教育程度、产前护理可及性、营养状况和环境影响等因素，预测有助于设计旨在降低婴儿死亡率的有效方案。
本研究论文旨在通过采用集成学习技术，特别是极端梯度提升（XGBoost）、堆叠、自适应提升和装袋法来预测印度的婴儿死亡率。分析数据来源于针对印度婴儿死亡率的全国性调查和人口统计学研究。收集到的数据经过了严格的预处理步骤，以便为预测建模做好准备。每个集成学习模型都应用于根据预处理后的数据预测婴儿死亡率。XGBoost可处理数据中的复杂非线性关系，堆叠模型用于进行准确且稳健的预测。自适应提升模型迭代训练多个弱学习器，从而使预测模型更强。自适应提升技术在有效解决类别不平衡问题的同时提高了弱分类器的性能。此外，实施装袋法以得出婴儿死亡率的线性和非线性关系。使用k折交叉验证对模型进行优化，以微调其超参数。通过使用不同的性能参数来分析集成技术的预测能力。
XGBoost取得了卓越的性能结果，准确率为98.75%，精确率为98.56%，召回率为98.24%。自适应提升模型强化了弱学习器并解决了类别不平衡问题，而装袋法捕捉到了线性和非线性关系。集成学习模型在预测婴儿死亡率方面显示出有效性，其中XGBoost在处理复杂非线性关系方面表现出色。
模拟结果表明，集成学习模型在预测印度婴儿死亡率方面非常有效，且存在显著的地区差异。例如，东北地区预测的婴儿死亡率最高，而南部地区最低。这些发现强调了在高死亡率地区进行针对性干预以减少差异的必要性。该研究突出了集成学习模型，特别是XGBoost在预测印度婴儿死亡率方面的有效性。研究结果强调了提高母亲教育程度、增加产前护理可及性以及减少社会经济差异的关键作用。
