Machine Learning Algorithms for understanding the determinants of under-five Mortality.
Machine Learning Algorithms for understanding the determinants of under-five Mortality.
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DOI：
10.1186/s13040-022-00308-8
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发表时间：
2022-09-24
期刊：
影响因子：
4.5
通讯作者：
Chilyabanyama, Obvious N.
中科院分区：
文献类型：
--
作者：
Saroj, Rakesh Kumar;Yadav, Pawan Kumar;Singh, Rajneesh;Chilyabanyama, Obvious N.
五岁以下儿童死亡率是任何国家儿童健康和社会发展的严重问题。该论文旨在发现机器学习模型在预测五岁以下儿童死亡率方面的准确性，并确定与五岁以下儿童死亡率相关的最重要因素。数据来自北方邦的全国家庭健康调查（NFHS-IV）。首先，我们使用多变量逻辑回归，因为它能够预测重要因素，然后我们使用机器学习技术，如决策树，随机森林，朴素贝叶斯，K-最近邻（KNN），逻辑回归，支持向量机（SVM），神经网络和岭分类器。通过混淆矩阵、准确度、精确度、召回率、F1评分、Cohen's Kappa和受试者工作特征曲线下面积（AUROC）检查每个模型的准确度。采用信息增益秩分析法寻找影响5岁以下儿童死亡率的重要因素。使用STATA-16.0、Python 3.3和IBM SPSS Statistics for Windows 27.0版软件进行数据分析。通过应用机器学习模型，结果显示，与其他预测模型相比，神经网络模型是五岁以下儿童死亡率的最佳预测模型，模型准确率为（95.29%至95.96%），回忆（71.51%至81.03%），精密度（36.64%~ 51.83%）、F1评分（50.46%~ 62.68%）、Cohen's Kappa值（0.48 ~ 0.60）、AUROC范围（93.51%~ 96.22%）和精确-召回曲线范围（99.52%~ 99.73%）。神经网络是最有效的模型，但逻辑回归也显示出预测五岁以下儿童死亡率的准确性（94%至95%）。AUROC范围（93.4%至94.8%）和精确-召回曲线（99.5%至99.6%）。活产儿童人数、存活时间、财富指数、出生时儿童大小、最近五年内的出生人数、曾经出生的儿童总数、母亲的教育水平和出生顺序被确定为影响五岁以下儿童死亡率的重要因素。与其他机器学习模型相比，神经网络模型在预测五岁以下儿童死亡率方面是一个更好的预测模型，但逻辑回归分析也显示了良好的结果。这些模型可能有助于健康研究中高维数据的分析。
Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality. The data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model’s accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen’s Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software. By applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen’s Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother’s education level, and birth order were identified as important factors influencing under-five mortality. The neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.
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影响因子：
3.5
作者：
Fenta HM;Zewotir T;Muluneh EK
通讯作者：
Muluneh EK
DOI：
10.1017/asb.2017.45
发表时间：
2018-05-01
期刊：
ASTIN BULLETIN
影响因子：
--
作者：
Hainaut, Donatien
通讯作者：
Hainaut, Donatien
影响因子：
2.4
作者：
Amoroso, Cheryl L.;Nisingizwe, Marie Paul;Ngabo, Fidele
通讯作者：
Ngabo, Fidele
影响因子：
2.9
作者：
Adegbosin, Adeyinka Emmanuel;Stantic, Bela;Sun, Jing
通讯作者：
Sun, Jing
影响因子：
4.8
作者：
Burges, CJC
通讯作者：
Burges, CJC