使用波桑回归和机器学习方法建模儿童1型糖尿病新病例的数量;沙特阿拉伯的一个案例研究
Ahood Alazwari1,2, Laleh Tafakori1, Alice Johnstone1
1School of Science, RMIT University, Melbourne, Victoria, Australia.
PloS one
|April 25, 2025
概括
沙特阿拉伯的儿童1型糖尿病 (T1D) 发病率是使用KPI建模的. 关键因素,如母亲的年龄和早期牛奶引入显著预测了新的T1D病例,帮助有针对性的预防策略.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 在医疗保健中的数据科学.
- 公共卫生流行病学 流行病学
背景情况:
- 1型糖尿病 (T1D) 在儿童中是一个日益严重的全球性问题.
- 对儿科T1D发病率需要有效的监测策略.
- 沙特阿拉伯面临着儿童T1D的增长率.
研究的目的:
- 在沙特阿拉伯,每月在儿童 (0-14岁) 中模拟T1D的新病例.
- 确定影响T1D发病率的关键绩效指标 (KPIs).
- 评估Poisson回归和机器学习模型的性能.
主要方法:
- 从2010年至2020年收集了非识别的T1D诊断数据 (n=377).
- 采用波桑回归,随机森林,SVM和KNN模型.
- 使用R平方,RMSE和MAE评估模型性能.
主要成果:
- 结合出生体重,产妇年龄,家族病史和营养的缩小模型是最佳的.
- 专注于母亲年龄 (>25岁) 和早期牛奶引入的模型显示出高预测准确度 (R平方0.80-0.83).
- 最好的模型实现了0.89和0.88的R平方值,低RMSE和MAE.
结论:
- 有关关键关键指标的简化模型可以有效预测儿童T1D发病率.
- 识别有影响力的关键关键指标有助于医疗保健提供者进行有针对性的监测.
- 这些发现支持制定战略,以减轻沙特阿拉伯儿童T1D病率上升的风险.
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