Explainable LightGBM Approach for Predicting Myocardial Infarction Mortality

arXiv:2404.15029v1 Announce Type: new
Abstract: 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 …

abstract arxiv challenges cs.lg data however improving information lightgbm machine machine learning machine learning techniques missing values mortality patient patients prediction risk type values vast