基于机器学习的乳腺癌风险预测数据准备方法:一个古巴案例研究
Jose Manuel Valencia-Moreno1, Everardo Gutierrez-Lopez1, Jose Angel Gonzalez-Fraga1
1Universidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.
MethodsX
|November 24, 2025
概括
这项研究提供了古巴妇女的开放乳腺癌风险因素数据集,以开发预测模型. 数据确保完整性,并支持用于公共卫生风险评估的机器学习.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 机器学习 机器学习
背景情况:
- 乳腺癌风险评估对公共卫生至关重要.
- 开发准确的预测模型需要高质量,可访问的数据集.
- 现有的数据集可能缺乏特定的人口或方法的严格性.
研究的目的:
- 展示古巴妇女乳腺癌风险因素的精选数据集.
- 促进乳腺癌风险预测模型的开发和验证.
- 支持机器学习在公共卫生和流行病学中的应用.
主要方法:
- 收集了2001年至2018年间1697名古巴妇女的数据.
- 实施了一种可重复的数据质量控制和变量丰富的方法.
- 确保数据完整性和与机器学习技术的兼容性.
主要成果:
- 现在可以获得乳腺癌风险因素的开放数据集.
- 预处理方法确保数据质量,可追溯性和一致性.
- 在前期处理后,在多个指标上实现了一致的预测模型性能.
结论:
- 该数据集是流行病学研究和风险评估的宝贵工具.
- 实施的方法确保了数据集适合于机器学习应用.
- 这个资源可以增强预防乳腺癌和早期检测的公共卫生战略.
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