从印度尼西亚新生儿人口中,预测2岁发育不良的预测模型
Devi Azriani1,2, Dwi Agustian3, Yenni Zuhairini3
1Doctoral Student, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia.
BMC pediatrics
|October 2, 2025
概括
这项研究开发了一种机器学习模型,用于预测印度尼西亚的儿童发育迟缓,识别高风险新生儿进行早期干预. k-最近的邻居模型获得了84.5%的F1得分,从而实现了有针对性的预防策略.
科学领域:
- 儿科健康 儿科健康
- 人工智能在医学中的应用
- 公共卫生信息学 公共卫生信息学
背景情况:
- 在像印度尼西亚这样的发展中国家,儿童发育迟缓仍然是一个重大的公共卫生挑战.
- 不足够的信息驱动的预防措施导致减少衰老率的进展缓慢.
- 这项研究解决了有效的早期检测和干预策略的需求.
研究的目的:
- 开发和验证一种预测模型,用于识别两岁时患缩风险的婴儿.
- 利用机器学习技术,在印尼新生儿群体中准确预测发育迟缓.
- 为了确定与缩相关的关键风险因素,针对性干预措施.
主要方法:
- 在数据挖掘跨行业标准流程 (CRISP-DM) 框架内使用机器学习算法.
- 利用了5093名儿童的数据和来自印尼家庭生活调查 (IFLS) 的23个预测变量.
- 使用F1分数,AUC,灵敏度,精度和混矩阵评估模型性能;通过多变量后勤回归开发了一个解释模型.
主要成果:
- k-最近邻居 (kNN) 模型以84.5%的F1得分展示了优越的预测性能,超过了其他测试的算法.
- 发育迟缓的关键预测因素包括出生体重,婴儿大小,母亲的年龄和身高,父亲的身高,母亲的教育水平,出生地点,所和废物处理标准.
- 最终的模型实现了分别为80.4%和86.8%的正和负预测值.
结论:
- 基于机器学习的预测模型可以有效地识别高风险的新生儿,以便及时,有针对性的干预.
- 将预测模型与专注于因果途径的规范方法整合在一起,对于可持续的衰老预防至关重要.
- 早期识别和干预是减轻衰老对健康和发育的长期影响的关键.
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