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Features selection and improving for trauma outcomes prediction models

机译:创伤结果预测模型的特征选择和改进

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Various demographic and medical factors have been linked with mortality after suffering from traumatic injuries such as age and post-injury disability. A considerable amount of literature has been published on the building of trauma prediction models. However, few analyse the features selection criteria. Patient records comprise a large amount of data and numerous variables, and some are more important than others. Highlighting the most influential variables and their correlations would assist in the better use of it. The intention of this study is to clarify several aspects of demographic and medical factors that could affect the outcome of trauma in order to exhibit the interaction between these factors and to represent their relationships. In addition, the aim is to use ranking and feature weights to select the features that increase accuracy and lead to better results.
机译:在遭受诸如年龄和受伤后残疾等外伤后,各种人口统计学和医学因素都与死亡率相关。关于创伤预测模型的建立,已经发表了大量文献。但是,很少分析特征选择标准。患者记录包含大量数据和众多变量,其中一些比其他的更为重要。突出显示最有影响力的变量及其相关性将有助于更好地使用它。这项研究的目的是阐明可能影响创伤后果的人口统计学和医学因素的几个方面,以展示这些因素之间的相互作用并表现出它们之间的关系。此外,目标是使用排名和特征权重来选择可以提高准确性并带来更好结果的特征。

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