线性回归与四种不同的机器学习方法在选择骨质疏松症风险因素的过程中,在中国女性老年人群中进行了对比
Shiow-Jyu Tzou1,2, Chung-Hsin Peng3, Li-Ying Huang4
1Teaching and Researching Center, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan, ROC.
Journal of the Chinese Medical Association : JCMA
|September 20, 2023
概括
机器学习模型可以准确地预测老年女性的骨矿物密度T-分数,将年龄,eGFR,BMI,尿酸和教育确定为关键风险因素. 这些先进的方法优于骨质疏松风险评估的传统回归方法.
科学领域:
- 老年学是指老年学的学科.
- 生物医学信息学 生物医学信息学
- 公共卫生 公共卫生
背景情况:
- 人口老龄化带来了重大挑战,骨质疏松症影响了个人和医疗保健系统.
- 骨质疏松风险因素的传统统计方法正在通过机器学习来增强.
- 之前的机器学习研究经常使用分类变量,失去定量信息.
研究的目的:
- 为了比较五种机器学习方法的预测准确性与骨矿物密度 (BMD) T-score的传统多重线性回归 (MLR).
- 识别和排名与BMDT分数相关的25个风险因素的重要性.
- 在老年人群中推进骨质疏松症的定量风险评估.
主要方法:
- 分析了24412名55岁以上的女性,25个相关变量.
- 应用MLR和五种机器学习算法:分类和回归树,天真贝叶斯,随机森林,随机梯度增强和极端梯度增强.
- 使用对称平均绝对百分比误差,相对绝对误差,根相对平方误差和根平均平方误差评估模型性能.
主要成果:
- 与MLR相比,机器学习方法在所有四个错误指标上都显示出更高的预测准确性.
- 年龄被确定为影响BMDT-score的最重要因素.
- 估计的淋巴细胞过率 (eGFR),体重指数 (BMI),尿酸 (UA) 和教育水平也被列为非常重要的风险因素.
结论:
- 机器学习模型为55岁以上女性的BMDT分数估计提供了更高的性能.
- 年龄,eGFR,BMI,UA和教育水平是这个人口群体骨矿物质密度的关键决定因素.
- 这项研究强调了先进的机器学习技术对定量骨质疏松风险评估的有用性.
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