根据国家流行病学乳腺癌预测:一种新的机器学习方法
Hasna El Haji1, Nada Sbihi1, Bassma Guermah1
1TICLab, International University of Rabat, Rabat, Morocco.
PloS one
|August 14, 2024
概括
乳腺癌发病率与饮食和饮酒等生活方式因素相关. 机器学习模型准确地预测了未来的乳腺癌发病率,有助于全球健康意识倡议.
科学领域:
- 在瘤学瘤学.
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 乳腺癌是全世界女性死亡的主要原因之一.
- 新出现的风险因素需要调查,以更好地了解和预防.
- 预测建模可以为乳腺癌控制的公共卫生策略提供信息.
研究的目的:
- 调查乳腺癌发病率与新发现的风险因素之间的相关性.
- 开发机器学习模型,用于预测国家一级乳腺癌发病率.
- 确定与乳腺癌趋势相关的关键生活方式和环境因素.
主要方法:
- 文献审查以确定新的乳腺癌风险因素.
- 从151个国家收集有关风险因素和发病率的数据.
- 斯皮尔曼的相关性分析和六个回归模型,包括CatBoost Regressor,用于预测.
主要成果:
- 在乳腺癌发病率和女性平均年龄,肉类消费,二氧化碳排放,抑郁,糖摄入量,烟草使用,牛奶消费,手机使用,酒精消费,农药暴露和口服避孕药使用之间发现了显著的正相关性.
- CatBoost Regressor模型实现了高预测准确度,其 R 平方值为 0.84 ± 0.03.
- 饮食习惯和生活方式的选择与乳腺癌发病率的增加密切相关.
结论:
- 鉴定的风险因素,特别是与饮食和生活方式相关的风险因素,对于了解乳腺癌趋势至关重要.
- 机器学习为全球预测乳腺癌发病率提供了一个强大的工具.
- 这些发现可以为有针对性的教育计划提供信息,以提高意识,并减轻高风险人群的乳腺癌风险.
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