Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database

TL;DR

XGBoost emerged as the superior model, achieving a test AUC-ROC of 0.9228 (95\% CI 0.8748 - 0.9613), significantly outperforming the previous work (AUC-ROC of 0.8766) and the best results reported in existing literature.

Abstract

Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not fully understood, indicating the need for more accurate prediction models. This study analyzed data from 1,177 patients over 18 years old from the MIMIC-III database, identified using ICD-9 codes. Preprocessing steps included handling missing data, removing duplicates, treating skewness, and using oversampling techniques to address data imbalances. Through rigorous feature selection using Variance Inflation Factor (VIF), expert clinical input, and ablation studies, 46 key features were identified to enhance model performance. Our analysis compared several machine learning models, including Logistic Regression, Support Vector Machine (SVM), Random Forest, LightGBM, and XGBoost. XGBoost emerged as the superior model, achieving a test AUC-ROC of 0.9228 (95\% CI 0.8748 - 0.9613), significantly outperforming our previous work (AUC-ROC of 0.8766) and the best results reported in existing literature (AUC-ROC of 0.824). The improved model's success is attributed to advanced feature selection methods, robust preprocessing techniques, and comprehensive hyperparameter optimization through Grid-Search. SHAP analysis and feature importance evaluations based on XGBoost highlighted key variables like leucocyte count and RDW, providing valuable insights into the clinical factors influencing mortality risk. This framework offers significant support for clinicians, enabling them to identify high-risk ICU heart failure patients and improve patient outcomes through timely and informed interventions.

Figures (9)

• Figure 1: Patient selection process for heart failure study, showing exclusions and final cohort size.
• Figure 2: Top 20 feature importances ranked by XGBoost, showing 'hypertensive' and 'Renal_failure' as the most influential features.
• Figure 3: Overview of the study workflow, including data preprocessing, feature extraction, modeling, and evaluation.
• Figure 4: AUC boxplot of the XGBoost model on the test dataset with all features included, achieving an AUC of 0.8450.
• Figure 5: AUC boxplot of the XGBoost model on the test dataset after removing the 'Heart Rate' feature, showing an increase in AUC to 0.8535.