用机器学习分析孟加拉儿童营养不良因素，精准识别高危人群
Risk factor identification and classification of malnutrition among under-five children in Bangladesh: Machine learning and statistical approach
- 采用随机森林和神经网络等4种算法建模，结合问卷数据分类营养不良程度
- 随机森林准确率达98.55%，神经网络达98.69%，均表现优异
- 发现喂养方式、腹泻史、母亲教育等关键风险因子，适合公共卫生决策参考
本研究基于孟加拉国2019年多指标集群调查（MICS-2019）数据，分析21,858名五岁以下儿童的营养不良影响因素，并运用决策树（DT）、随机森林（RF）、支持向量机（SVM）和多层感知机（MLP）四种机器学习算法对营养不良阶段进行分类。通过准确率、精确率、召回率和F1分数评估模型性能。统计分析显示，体重/年龄Z评分（WAZ2，-0.828）、体重/身高Z评分（WHZ2，-0.706）、BMI Z评分（ZBMI，-0.656）及是否仍在哺乳（BD3，-0.59）为最显著负相关因素。其中，随机森林准确率为98.55%，平均精确率98.3%，召回率95.68%，F1分数97.13%；多层感知机准确率98.69%，平均精确率97.62%，召回率90.96%，F1分数97.39%，表现突出。
原文摘要 · Abstract (English)
This study aims to understand the factors that resulted in under-five children's malnutrition from the Multiple Indicator Cluster (MICS-2019) nationwide surveys and classify different malnutrition stages based on the four well-established machine learning algorithms, namely - Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) neural network. Accuracy, precision, recall, and F1 scores are obtained to evaluate the performance of each model. The statistical Pearson correlation coefficient analysis is also done to understand the significant factors related to a child's malnutrition. The eligible data sample for analysis was 21,858 among 24,686 samples from the dataset. Satisfactory and insightful results were obtained in each case and, the RF and MLP performed extraordinarily well. For RF, the accuracy was 98.55%, average precision 98.3%, recall value 95.68%, and F1 score 97.13%. For MLP, the accuracy was 98.69%, average precision 97.62%, recall 90.96%, and F1 score of 97.39%. From the Pearson co-efficient, all negative correlation results are enlisted, and the most significant impacts are found for the WAZ2 (Weight for age Z score WHO) (-0.828"), WHZ2 (Weight for height Z score WHO) (-0.706"), ZBMI (BMI Z score WHO) (-0.656"), BD3 (whether child is still being breastfed) (-0.59"), HAZ2 (Height for age Z score WHO) (-0.452"), CA1 (whether child had diarrhea in last 2 weeks) (-0.34"), Windex5 (Wealth index quantile) (-0.161"), melevel (Mother's education) (-0.132"), and CA14/CA16/CA17 (whether child had illness with fever, cough, and breathing) (-0.04) in successive order.
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