Prediction of In-Hospital Mortality for ICU Patients with Heart Failure

Heart failure affects millions of people worldwide. It greatly reduces quality of life and is associated with high mortality rates. Despite extensive research, the statistical connection between heart failure and mortality rates for ICU patients remains underexplored, indicating the need for improved prediction models. This study identified 1,177 patients over 18 years old from the MIMIC-III database using ICD-9 codes. Preprocessing consisted of handling missing data, deleting duplicates, treating skewness, and oversampling to alleviate data imbalances. 18 features were selected within a LightGBM model by checking Variance Inflation Factor (VIF) values, LASSO Regression, and univariate analysis. The final output of the LASSO Logistic Regression model had the highest test AUC-ROC of 0.8766 (95% CI 0.8065 - 0.9429) and accuracy of 0.7291 compared to other baseline models, including Logistic Regression, Random Forest, LightGBM, Support Vector Machine (SVM), and Decision Trees. All models demonstrated good calibration with relatively low Brier scores, highlighting their reliability in predicting in-hospital mortality. Our models predicted deaths of heart failure ICU patients better than the best results found in both literature and baseline models. These results were based on preprocessing missing values via improved imputation strategies and improved feature selection based on an expanded literature search and improved experiences selecting key features. With the Grid-Search, we had a near-perfect predictive model. These methods greatly increased the predictive accuracy of in-hospital mortality in ICU patients with heart failure.