机器学习预测塞拉利昂的袋鼠母婴护理:特征选择技术和分类算法的比较研究
Augustus Osborne1, Afeez A Soladoye2, Kobloobase O Usani3
1Institute for Development, Western Area, Freetown, the Republic of Sierra Leone.
International journal of medical informatics
|November 1, 2025
概括
机器学习准确地预测了塞拉利昂的袋鼠母亲护理 (KMC) 实践. 这一发现有助于为低出生体重婴儿制定有针对性的母婴健康干预措施.
科学领域:
- 孕产妇和儿童的健康
- 机器学习应用 机器学习应用
- 公共卫生干预 公共卫生干预
背景情况:
- 袋鼠母亲护理 (KMC) 对新生儿的结果至关重要,特别是对于低出生体重的婴儿.
- 识别KMC实践的预测因素对于有效的健康战略和政策至关重要.
- 塞拉利昂在新生儿护理方面面临着挑战,因此确定KMC预测因子成为优先事项.
研究的目的:
- 在塞拉利昂确定母护理 (KMC) 实践的关键预测因素.
- 评估用于KMC预测的各种特征选择和机器学习算法的有效性.
- 为了利用2019年塞拉利昂人口和健康调查数据进行预测建模.
主要方法:
- 分析了来自2019年塞拉利昂人口和健康调查的7377份母婴健康记录.
- 应用三个特征选择技术:自适应性群优化 (ACO),递归特征消除 (RFE) 和逆向特征选择.
- 实施七种分类算法,包括随机森林,XGBoost和整体方法,用于类不平衡和交叉验证的SMOTE.
主要成果:
- 随机森林和XGBoost在所有测试的特征选择方法中都表现出卓越的性能.
- 通过多种选择技术识别的共识特征产生了高精度 (0.72) 和F1分数 (0.78) 随机森林和XGBoost.
- 逆向特征选择和ACO在识别KMC的显著预测特征方面被证明比RFE更有效.
结论:
- 机器学习模型,特别是集体方法,显示出KMC实践的强大预测能力.
- 综合的特征选择提高了预测模型的准确性和可靠性.
- 这些发现为在塞拉利昂设计有针对性的母婴健康干预提供了宝贵的见解.
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