Scientia Iranica

Scientia Iranica

In-hospital mortality prediction model of heart failure patients using imbalanced registry data: A machine learning approach

Document Type : Research Article

Authors
Hadi Sabahi 1
Mansour Vali 1
Davood Shafie 2
1 Department of Biomedical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
2 Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.
10.24200/sci.2023.61637.7412
Abstract
Heart Failure (HF) is a cardiac dysfunction disease with a high mortality rate that is mostly calculated via registry data. The objective of this work was to predict in-hospital mortality in patients hospitalized with HF utilizing their pre-hospitalization registry data. The data include 3968 HF records extracted from the Persian Registry of cardio Vascular diseasE (PROVE)/HF registry. We proposed a method that contains an imbalanced ensemble probabilistic model which using registry data to predict HF patients who die during hospitalization from those who survive. The suggested ensemble model uses machine learning models, namely Decision Tree, Random Forest, Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), which were evaluated. We also used feature importance analysis to identify the important features and reduce the complexity. The results illustrated that the proposed method can predict in-hospital mortality of HF patients using XGBoost that outperformed all others. The feature importance ranking obtained by XGBoost demonstrated that the proposed method can achieve acceptable performance with the first 18 important features and XGBoost (accuracy: 76.4%±1.6%, sensitivity: 76.8%±6.9%, specificity: 76.4%±1.8%). Moreover, statistical analysis presented significant predictors of in-hospital mortality (P-value<0.01). In conclusion, the proposed method can effectively predict in-hospital mortality of HF patients using the imbalanced data.
Keywords
Heart Failure (HF)
In-hospital mortality
Registry data
Imbalanced data
Machine learning
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Scientia Iranica
Volume 32, Issue 17
Transactions on Computer Science & Engineering and Electrical Engineering
September and October 2025 Article ID:7412

  • Receive Date 22 December 2022
  • Revise Date 17 May 2023
  • Accept Date 17 July 2023