使用机器学习对生活方式和遗传数据进行癌症风险预测
Mohamed Abdelmoaty Ahmed1, Ahmed AbdelMoety2, Asmaa Mohamed Ahmed Soliman1,3
1Faculty of Medicine, Merit University, Sohag, Egypt.
Scientific reports
|August 19, 2025
概括
机器学习模型可以使用遗传和生活方式数据预测癌症风险. 分类提升实现了98.75%的准确性,突出了其早期癌症检测和个性化预防策略的潜力.
科学领域:
- 计算生物学是一种计算生物学.
- 在瘤学瘤学.
- 数据科学是数据科学.
背景情况:
- 癌症是全球主要的死亡原因,早期检测对于改善患者的治疗结果至关重要.
- 整合遗传倾向和生活方式选择是提高癌症风险预测的关键.
研究的目的:
- 研究机器学习 (ML) 技术在预测癌症风险方面的有效性.
- 用一个全面的患者信息数据集来评估各种ML算法.
主要方法:
- 研究人员利用了1200个患者记录的数据集,包括人口统计,生活方式和遗传因素.
- 一个端到端的ML管道涉及数据探索,预处理,特征缩放和模型训练.
- 使用分层交叉验证和单独的测试集,比较了9个监督学习算法.
主要成果:
- 分类提升 (CatBoost) 显示出卓越的性能,测试准确率为98.75%,F1得分为0.9820.
- 个人癌症史,遗传风险和吸烟状况被确定为重要的预测因素.
- 基于增强的组合模型有效地捕获了复杂的健康数据相互作用.
结论:
- ML,特别是CatBoost,对准确的个性化癌症风险评估有显著的希望.
- 将遗传和可修改的生活方式因素集成到预测模型中,可以增强早期检测策略.
- 这种方法支持主动和个性化的预防性医疗保健的发展.
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