通过智能算法和古巴妇女的风险因素估计乳腺癌风险
Jose Manuel Valencia-Moreno1, Jose Angel Gonzalez-Fraga2, Everardo Gutierrez-Lopez3
1Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico; Universidad de las Ciencias Informáticas, La Habana, Cuba.
Computers in biology and medicine
|July 11, 2024
概括
一个新的机器学习模型有效地估计了古巴妇女的乳腺癌风险,超过了美国西班牙裔妇女使用的模型. 这种工具有助于早期检测和风险识别,可能降低死亡率.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 乳腺癌是全球癌症死亡的主要原因,不成比例地影响女性.
- 现有的风险预测模型对于特定人群缺乏效率,需要量身定制的方法.
- 对古巴妇女进行校准的乳腺癌预测模型对于有效的早期检测至关重要.
研究的目的:
- 提出和评估一个概念机器学习模型,用于估计古巴妇女乳腺癌风险.
- 确定最有效的机器学习算法来预测这一群体的乳腺癌风险.
- 评估该模型的通用性和降低拉丁美洲乳腺癌死亡率的潜力.
主要方法:
- 开发一个概念模型,有三个组成部分:知识表示,风险估计建模和风险预测因素评估.
- 应用九种常见的机器学习算法来生成风险预测器.
- 利用了两个数据集:一个来自古巴妇女,另一个来自美国西班牙裔妇女 (乳腺癌监测联盟).
主要成果:
- 随机森林算法证明了古巴妇女的优异性能,达到5.981的加权得分,训练精度为0.996,训练AUC为0.997.
- 使用古巴妇女数据开发的风险预测器与使用美国西班牙裔妇女数据开发的风险预测器相比,其准确性和AUC值更高.
- 该模型有效地估计了乳腺癌风险,显示了早期检测和患者风险分层的潜力.
结论:
- 拟议的机器学习模型是估计古巴妇女乳腺癌风险的宝贵工具.
- 该模型与古巴数据的卓越性能表明,该模型可能对其他西班牙裔人口具有普遍性.
- 实施提供了一个经济可行的战略,以降低乳腺癌死亡率在拉丁美洲国家.
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