标题： 机器学习中的常见步骤可能会阻碍医学领域的可解释性目标 显示英文标题

标题： Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine

摘要： 数据预处理是机器学习中的一个重要步骤，可以提高模型的性能并减少运行时间。 这可能包括处理缺失值、异常值检测和移除、数据增强、降维、数据规范化以及处理混杂变量的影响。 尽管发现这些步骤提高了模型的准确性，但如果它们没有被仔细考虑，特别是在医学领域，可能会阻碍模型的可解释性。 当不当实施缺失值和异常值的移除时，它们可能会阻止新发现。 此外，当做出决策时，它们可能会使模型对模型中的所有组不公平。 此外，它们会将特征转化为无单位且在临床上无意义的特征，从而导致不可解释性。 本文讨论了机器学习中数据预处理的常见步骤及其对模型可解释性和可解释性的影响。 最后，本文讨论了一些可能的解决方案，可以在不降低模型可解释性的情况下提高模型的性能。 显示英文摘要

摘要： Data pre-processing is a significant step in machine learning to improve the performance of the model and decreases the running time. This might include dealing with missing values, outliers detection and removing, data augmentation, dimensionality reduction, data normalization and handling the impact of confounding variables. Although it is found the steps improve the accuracy of the model, but they might hinder the explainability of the model if they are not carefully considered especially in medicine. They might block new findings when missing values and outliers removal are implemented inappropriately. In addition, they might make the model unfair against all the groups in the model when making the decision. Moreover, they turn the features into unitless and clinically meaningless and consequently not explainable. This paper discusses the common steps of the data preprocessing in machine learning and their impacts on the explainability and interpretability of the model. Finally, the paper discusses some possible solutions that improve the performance of the model while not decreasing its explainability.