巴西癌症死亡率的空间集群:一种机器学习建模方法
Bruno Casaes Teixeira1, Tatiana Natasha Toporcov1, Francisco Chiaravalloti-Neto1
1Department of Epidemiology, Faculty of Public Health, University of São Paulo, São Paulo, Brazil.
International journal of public health
|August 7, 2023
概括
机器学习准确地预测了巴西的癌症死亡率,识别了过度癌症死亡率的地理集群. 主要预测因素包括人口统计和计算机访问,突出区域差异.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 癌症死亡率 (CM) 在地理位置上有所不同.
- 生态层面的CM预测可以为公共卫生干预提供信息.
- 确定癌症过度死亡率 (eCM) 的空间集群对于有针对性的策略至关重要.
研究的目的:
- 评估机器学习算法来预测巴西的生态水平癌症死亡率.
- 为了确定癌症死亡率过高的统计学意义上的空间集群.
- 分析预测特征和癌症类型,以获得有针对性的见解.
主要方法:
- 利用来自巴西官方数据库的年龄标准化癌症死亡率数据.
- 使用机器学习算法 (例如,梯度增强树) 在70%的数据上进行训练,并在30%的数据上进行测试.
- 使用SatScan识别了癌症过度死亡率集群,对10种主要癌症类型进行了单独分析.
主要成果:
- 梯度增强树实现了最高的预测准确性 (R2 = 0.66).
- 在总癌症 (巴格),食道癌症 (西里奥格兰德杜苏尔州) 和胃癌 (马卡帕州) 发现了大量的癌症死亡率.
- 社会人口统计学变量,特别是拥有计算机的白人人口和居民的百分比,是关键预测因素.
结论:
- 机器学习模型可以在生态水平上有效预测癌症死亡率.
- 确定了巴西的不同地理区域,癌症死亡率明显高于预期.
- 这些发现强调了数据驱动方法的潜力,以精确确定需要集中癌症控制工作的领域.
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