机器学习分类算法的比较及其在弗雷明汉心脏研究中的应用
Comparison of Machine Learning Classification Algorithms and Application to the Framingham Heart Study
AI总结：
本文以弗雷明汉冠心病数据为案例，比较八种机器学习分类算法在四种训练/测试场景下的泛化性，提出I型双重判别评分方法并证明其最优，同时给出提取最优变量层级的方法。
摘要：
在医疗保健中使用机器学习算法可能放大社会不公与健康不平等。虽然偏见的加剧可能在问题选择、数据收集和结局定义阶段发生并累积，但本研究关注的是机器学习分类算法在开发过程和部署后出现的一些泛化性障碍。以弗雷明汉冠心病数据作为案例研究，我们展示了如何有效选择概率截断值，将针对二分类变量的回归模型转换为分类器。随后，我们在四种训练/测试场景下比较了八种机器学习分类算法预测性能的抽样分布，以检验它们的泛化能力及其延续偏见的潜在风险。我们表明，极端梯度提升（Extreme Gradient Boosting）和支持向量机（Support Vector Machine）在不平衡数据集上训练时均存在缺陷。我们引入并证明，I型双重判别评分（double discriminant scoring of type I）最具泛化性，因为无论训练/测试场景如何，它都持续优于其他分类算法。最后，我们引入了一种提取分类算法最优变量层级的方法，并在总体、男性和女性的弗雷明汉冠心病数据上进行了说明。
英文摘要：
The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and compound during the problem selection, data collection, and outcome definition, this research pertains to some generalizability impediments that occur during the development and the post-deployment of machine learning classification algorithms. Using the Framingham coronary heart disease data as a case study, we show how to effectively select a probability cutoff to convert a regression model for a dichotomous variable into a classifier. We then compare the sampling distribution of the predictive performance of eight machine learning classification algorithms under four training/testing scenarios to test their generalizability and their potential to perpetuate biases. We show that both the Extreme Gradient Boosting, and Support Vector Machine are flawed when trained on an unbalanced dataset. We introduced and show that the double discriminant scoring of type I is the most generalizable as it consistently outperforms the other classification algorithms regardless of the training/testing scenario. Finally, we introduce a methodology to extract an optimal variable hierarchy for a classification algorithm, and illustrate it on the overall, male and female Framingham coronary heart disease data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
