用自然秩序对准的真实世界前列腺癌数据的推算方法的比较
Susanne Schmitt1,2, Franz Rothlauf1
1Johannes Gutenberg University, Mainz, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
通过链式方程 (MICE) 进行多重推算,随机森林 (mice_rf) 最好处理缺少的癌症数据. 这种归算方法提高了精度,相比 k 最近邻 (knn) 和后勤回归,特别是更多的缺失数据.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 缺失值 (NA) 在癌症研究中很常见,影响了预测模型和分析.
- 订购的分类数据,如瘤分级和阶段,需要专门的归算方法.
- 不完整的患者数据可能会影响风险分类和生存时间分析.
研究的目的:
- 为了比较在癌症研究中处理缺失数据的各种归算方法的性能.
- 在现实前列腺癌数据集上评估归算技术.
- 确定对有序的分类癌症数据最准确的归算方法.
主要方法:
- 通过链式方程 (MICE) 进行的多重推算中,比较模式归算,k 最近邻 (knn),带有比例概率的逻辑回归 (mice_polr) 和随机森林 (mice_rf).
- 利用来自德国莱兰-柏林州癌症登记处的前列腺癌数据集.
- 模拟完全随机缺失 (MCAR) 数据在10%,20%,30%和50%的水平.
主要成果:
- 随机森林归算 (mice_rf) 在所有缺失数据百分比中显著优于其他方法.
- 最接近的邻居 (knn) 的表现中等,而后勤回归 (mice_polr) 的表现最差.
- 在较少的类别,均的类别分布或占主导地位的类别中,归算准确性得到改善.
结论:
- 随机森林的MICE是一个非常有效的归算策略,用于有序的分类癌症数据.
- 计算方法的选择显著影响了癌症数据分析的准确性.
- 数据特征,如类别分布,影响归算方法的性能.
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