Park Youngho
Department of Big Data Application, College of Smart Interdisciplinary Engineering, Hannam University, Daejeon, Korea.
J Minim Invasive Surg. 2024 Sep 15;27(3):129-137. doi: 10.7602/jmis.2024.27.3.129.
Recently, interest in machine learning (ML) has increased as the application fields have expanded significantly. Although ML methods excel in many fields, establishing an ML pipeline requires considerable time and human resources. Automated ML (AutoML) tools offer a solution by automating repetitive tasks, such as data preprocessing, model selection, hyperparameter optimization, and prediction analysis. This review introduces the use of AutoML tools for general research, including clinical studies. In particular, it outlines a simple approach that is accessible to beginners using the R programming language (R Foundation for Statistical Computing). In addition, the practical code and output results for binary classification are provided to facilitate direct application by clinical researchers in future studies.
近年来,随着机器学习(ML)应用领域的显著扩展,人们对它的兴趣与日俱增。尽管ML方法在许多领域表现出色,但建立一个ML流程需要大量的时间和人力资源。自动化机器学习(AutoML)工具通过自动化诸如数据预处理、模型选择、超参数优化和预测分析等重复性任务提供了一种解决方案。本综述介绍了AutoML工具在包括临床研究在内的一般研究中的应用。特别是,它概述了一种使用R编程语言(R统计计算基金会)的、初学者也能掌握的简单方法。此外,还提供了二元分类的实际代码和输出结果,以便临床研究人员在未来的研究中直接应用。