Myocardial Infarction is a main cause of mortality globally, and accurate risk prediction is crucial for improving patient outcomes. Machine Learning techniques have shown promise in identifying high-risk patients and predicting outcomes. However, patient data often contain vast amounts of information and missing values, posing challenges for feature selection and imputation methods. In this article, we investigate the impact of the data preprocessing task and compare three ensembles boosted tree methods to predict the risk of mortality in patients with myocardial infarction. Further, we use the Tree Shapley Additive Explanations method to identify relationships among all the features for the performed predictions, leveraging the entirety of the available data in the analysis. Notably, our approach achieved a superior performance when compared to other existing machine learning approaches, with an F1-score of 91,2% and an accuracy of 91,8% for LightGBM without data preprocessing.

在这篇文章中，我们研究了数据预处理任务的影响，并比较了三种集成增强树方法来预测心肌梗塞患者的死亡风险。此外，我们采用树Shapley加性解释方法，来识别执行预测所涉及特征之间的关系，利用所有可用数据进行分析。值得注意的是，我们的方法在与其他现有机器学习方法的比较中取得了卓越的性能，LightGBM在无数据预处理的情况下的F1-score为91.2％，准确度为91.8％。

可解释的 LightGBM 预测心肌梗死死亡率