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摘要翻译：
本文提出了基于随机森林分类器、支持向量机、梯度提升决策树和人工神经网络的机器学习模型，用于预测韩国癌症筛查项目的参与度。性能最好的模型是基于梯度提升决策树的，接收机工作特性曲线下面积（AUC-ROC）为0.8706，平均精度为0.8776。这项研究的结果令人鼓舞，并表明随着进一步的研究，这些模型可以直接应用于韩国的医疗保健系统，从而提高韩国国家癌症筛查计划的参与度。
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英文标题：
《Predicting Participation in Cancer Screening Programs with Machine
Learning》
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作者：
Donghyun Kim
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最新提交年份：
2021
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
In this paper, we present machine learning models based on random forest classifiers, support vector machines, gradient boosted decision trees, and artificial neural networks to predict participation in cancer screening programs in South Korea. The top performing model was based on gradient boosted decision trees and achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.8706 and average precision of 0.8776. The results of this study are encouraging and suggest that with further research, these models can be directly applied to Korea's healthcare system, thus increasing participation in Korea's National Cancer Screening Program.
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PDF链接：
https://arxiv.org/pdf/2101.11614
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