预测与糖尿病和高血压相关的膀癌的机器学习算法:NHANES 2009年至2018年
1Department of Urology, Wuhan Fourth Hospital, Wuhan, China.
Medicine
|January 26, 2024
概括
糖尿病与更高的膀癌风险有关. 机器学习,特别是XGBoost模型,在预测膀癌的发展方面表现有前途,有助于早期检测和干预策略.
科学领域:
- 在瘤学瘤学.
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 膀癌是一个全球性的健康问题,在全球最常见的十大癌症中排名第一.
- 糖尿病,高血压和膀癌风险之间的关联仍然不完全理解,需要进一步调查.
- 有限的研究已经探索了机器学习模型的应用,用于预测膀癌症的发展.
研究的目的:
- 为了研究糖尿病,高血压和膀癌之间的关系.
- 开发和评估用于预测膀癌的机器学习模型.
- 识别潜在的风险因素,改善膀癌的早期检测.
主要方法:
- 利用了来自国家健康和营养检查调查的1789名患者的数据.
- 采用多变量逻辑回归来检查关联,调整混因素.
- 为了预测性能,比较了四种机器学习模型 (XGBoost,人工神经网络,随机森林,支持矢量机器).
主要成果:
- 高龄和男性性别与更高的膀癌发病率有关.
- 发现糖尿病与膀癌的风险增加显著相关 (OR=1.24,95% CI:1.17-3.02).
- XGBoost模型在预测膀癌方面表现出卓越的性能,达到高精度 (0.978) 和曲线下面积 (0.78).
结论:
- 糖尿病是与膀癌相关的重要危险因素.
- 机器学习模型XGBoost在预测膀癌方面非常有效.
- 这些发现可以为预防和管理膀癌的临床实践和公共卫生策略提供信息.
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