使用机器学习预测2型糖尿病发病年龄:KSA的一个案例研究
Faten Al-Hussein1,2, Laleh Tafakori1, Mali Abdollahian1
1School of Science, RMIT University, Melbourne, Victoria, Australia.
PloS one
|February 11, 2025
概括
在沙特阿拉伯预测2型糖尿病 (T2D) 发病年龄对于早期干预至关重要. 机器学习模型识别了诸如脂质概况和BMI等关键因素,有助于为这种流行疾病制定主动的医疗保健策略.
科学领域:
- 内分泌学和新陈代谢学
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 沙特阿拉伯的2型糖尿病 (T2D) 患病率正在上升,这给医疗保健带来了重大挑战.
- 早期预测T2D发病时的年龄对于及时干预和减少并发症至关重要.
- 沙特阿拉伯在T2D患病率方面在全球排名第7位,这凸显了区域研究的必要性.
研究的目的:
- 在沙特阿拉伯预测2型糖尿病 (T2D) 发病时的年龄.
- 通过各种机器学习模型,确定影响T2D发病年龄的关键预测因素.
- 为医疗保健从业人员提供监测和干预策略的工具.
主要方法:
- 使用多重线性回归 (MLR),人工神经网络 (ANN),随机森林 (RF),支持向量回归 (SVR) 和决策树回归 (DTR).
- 开发了使用1000名糖尿病患者 (2018-2022) 医疗记录的模型,包括人口统计,生活方式和脂质资料数据.
- 在模型开发中使用了发病年龄的对数转换.
主要成果:
- 平均T2D发病年龄为65岁,最常见的范围在40至90岁之间.
- MLR和RF模型表现出最好的性能,R2值分别为0.90和0.89.
- 发现的关键预测因素包括甘油三,总胆固醇,高密度胆固醇,费里丁,BMI,SBP,WBC,饮食和维生素D水平.
结论:
- 这项研究是沙特阿拉伯首次应用MLR,ANN,RF,SVR和DTR来预测T2D发病年龄.
- 开发的模型为预测T2D发病和识别风险人群提供了有价值的工具.
- 调查结果可以为针对性的干预策略提供信息,以减轻沙特阿拉伯T2D的影响.
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