使用基于机器学习的预测模型对膀癌的生活方式和职业风险评估
Naser Shakhssalim1, Atefeh Talebi2, Mohammad-Taha Pahlevan-Fallahy3
1Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Cancer reports (Hoboken, N.J.)
|July 5, 2023
概括
机器学习模型有效预测膀癌风险. 随机森林模型表现最好,识别了感染,吸烟和饮食等关键风险因素,用于早期检测和预防策略.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 膀癌是一个重大的全球健康问题,导致大量的发病率和死亡率.
- 膀对环境因素和炎症的持续暴露有助于癌症的发展.
研究的目的:
- 开发和评估用于膀癌风险预测的机器学习 (ML) 模型.
- 确定影响膀癌概率的关键风险因素.
主要方法:
- 一项基于人口的病例控制研究,涉及692例膀癌病例和692例对照.
- 各种ML算法的应用和性能评估,包括神经网络 (NN),随机森林 (RF),决策树 (DT),天真贝斯 (NB),梯度增强 (GB) 和后勤回归 (LR).
主要成果:
- 随机森林 (RF) 模型实现了最高的性能,AUC为0.86,精度为79%.
- 通过RF识别的关键预测因素包括复发性感染,膀结石史,吸烟,慢性功能衰竭和饮食习惯.
结论:
- 机器学习模型可以准确预测膀癌的概率.
- 风险预测整合了病史,职业暴露和生活方式因素,有助于早期检测和预防.
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