The main reason that hinders early treatment of ACS patients is delayed patient decision-making (PD). In order to explore the delay factors of patients with ACS, this paper builds a machine learning-based analysis model of delay factors for patients with acute coronary syndrome based on machine learning. Moreover, this paper combines structural equations to analyze the factors affecting accidents, and uses the generalized ordered logit model in statistics and the popular random forest model in machine learning to establish the analysis models of the delay factors of acute coronary syndromes, and analyze the functional structure of the models. In addition, this paper obtains data through actual survey methods, and analyzes the data through the model constructed in this paper to explore the risk factors that affect the delay in seeking medical treatment, which is presented through charts. The research results show that the model constructed in this paper is more reliable and can be applied in practice.

中文翻译：

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基于机器学习的山区急性冠脉综合征就诊延误的危险因素

阻碍ACS患者早期治疗的主要原因是患者的决策延迟（PD）。为了探索ACS患者的延迟因素，本文建立了基于机器学习的基于机器学习的急性冠脉综合征患者延迟因素分析模型。此外，本文结合结构方程来分析影响事故的因素，并使用统计中的广义有序logit模型和机器学习中的流行随机森林模型来建立急性冠脉综合征延迟因子的分析模型，并分析其功能。模型的结构。另外，本文通过实际调查方法获得数据，并通过本文构建的模型对数据进行分析，以探索影响就诊延迟的风险因素，并通过图表显示。研究结果表明，本文构建的模型更加可靠，可以在实践中应用。