使用机器学习探索巴基斯坦儿童营养不良的风险因素和预测模型
Muhammad Usman Saleem1, Muhammad Usman Aslam2, Abdul Ghani Khatir1
1School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
概括
机器学习模型能够有效地预测巴基斯坦儿童营养不良, 这些发现支持针对弱势群体的针对性干预.
科学领域:
- 儿童营养
- 计算流行病学
- 公共卫生信息学
背景情况:
- 儿童营养不良,包括发育迟缓,衰减和体重不足, 仍然是巴基斯坦的一个严重的公共卫生问题.
- 社会人口和健康相关因素对五岁以下儿童的营养不良影响很大.
研究的目的:
- 确定与巴基斯坦儿童营养不良相关的危险因素.
- 开发和评估用于预测儿童营养不良的机器学习模型 (发育迟缓,衰减,体重不足).
- 提供有针对性的公共卫生干预信息.
主要方法:
- 巴基斯坦人口和健康调查 (PDHS) 2017-2018年数据的横截面分析.
- 用于识别风险因素的物流回归.
- 使用交叉验证和火车测试分割的随机森林,SVM,Naïve Bayes和AdaBoost模型的应用和性能评估.
主要成果:
- 血缘关系的婚姻,较低的财富,母亲的教育水平低,以及地区差异 (辛德邦,巴鲁吉斯坦) 被认为是显著的风险因素.
- 随机森林模型在预测营养不良指标方面取得了最高的准确性和特异性.
- 支持矢量机 (SVM) 对于肥胖和体重不足的儿童表现出更高的灵敏度.
结论:
- 巴基斯坦的儿童营养不良与社会人口因素的复杂相互作用有关.
- 机器学习为预测儿童营养不良提供了一种强有力的方法.
- 干预措施应优先考虑高风险地区的孕产妇教育,卫生和减少贫困.
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