HFS‐LightGBM: A machine learning model based on hybrid feature selection for classifying ICU patient readmissions

Abstract

AbstractCompared to patients readmitted to general wards, readmitted patients in the intensive care unit (ICU) are exposed to higher mortality rates and prolonged hospital stays. Moreover, the readmission of ICU patients brings pressing challenges for ICU management. Most models are devoted to identifying the risk factors and developing classification models that can predict whether ICU patients will be readmitted. Though these models are prominent, they do not provide estimates for the frequency of readmissions. This paper establishes a prediction model, hybrid feature selection‐LightGBM (HFS‐LightGBM), to evaluate the probability and frequency of ICU patient readmissions empirically. In terms of feature selection, a hybrid feature selection (HFS) algorithm for LightGBM combines the filter and wrapper methods. Pearson's correlation coefficient is employed in the filter procedure. Then we adopt the targeted LightGBM classifier along with the recursive feature elimination and cross‐validated (RFECV) to produce the optimal feature subset. Additionally, the hyperparameters of the HFS‐LightGBM are optimized. The HFS‐LightGBM is employed on the real‐world ICU dataset containing 1722 patients' electronic health records. This model outperforms the current prevailing readmission models. The identified frequency can assist doctors in making specific interventions for patients to reduce the ICU readmission rate.