Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! By
Improving Prediction Of Mortality In Icu Via Fusion Of Selectkbest With Smote Method And Extra Tree Classifier Publisher



M Maftoun MOHAMMAD ; Jh Hassannataj Joloudari Javad HASSANNATAJ ; O Zare OMID ; M Khademi MARYAM ; A Atashi ALIREZA ; Ma Nematollahi Mohammad ALI ; R Alizadehsani ROOHALLAH ; Jm Gorriz J M
Authors

Source: Lecture Notes in Computer Science Published:2024


Abstract

The healthcare system’s increasing adoption of analytics and advanced computers demonstrates a growing tendency to develop a strong prognostication mechanism based on Artificial Intelligence (AI), utilizing technology to explore hidden relations between data and assessment in medical contexts. Hence, predicting the mortality of Intensive Care Unit (ICU) patients is a vital yet challenging task with significant implications for clinical decision-making in healthcare. This study aims to present an effective approach based on the AutoML framework for building Machine Learning (ML) models to predict mortality. PyCaret was applied as an AutoML framework in this study. ML approaches such as Extra Tree Classifier (ET), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Classifier (GBC), and Adaptive Boosting (AdaBoost), along with feature selection technique, SMOTE, and Auto Fine Tuned via Grid-Random search (GRS) by the assistance of PyCaret are employed to predict mortality. Results demonstrate that the hybrid (SelectKBest + SMOTE + ET) method achieves the highest AUROC of 93.54% for prediction, outperforming other models. © 2024 Elsevier B.V., All rights reserved.
Related Docs
View other Related Docs
1. Risk Factors Associated With In-Hospital Mortality in Iranian Patients With Covid-19: Application of Machine Learning, Polish Journal of Medical Physics and Engineering (2022)
2. Machine Learning-Based Predictive Modeling of Angina Pectoris in an Elderly Community-Dwelling Population: Results From the Pocosteo Study, PLOS ONE (2025)
3. Optimizing Cardiovascular Disease Mortality Prediction: A Super Learner Approach in the Tehran Lipid and Glucose Study, BMC Medical Informatics and Decision Making (2024)
Experts (# of related papers)
View all Related Experts
Alireza Atashi (6)
Kaveh Hosseini (3)
Other Related Docs
4. Risk Predictions of Hospital-Acquired Pressure Injury in the Intensive Care Unit Based on a Machine Learning Algorithm, International Wound Journal (2023)
5. Investigating the Performance of Machine Learning Algorithms in Predicting the Survival of Covid-19 Patients: A Cross Section Study of Iran, Health Science Reports (2023)
6. Prediction of Neonatal Deaths in Nicus: Development and Validation of Machine Learning Models, BMC Medical Informatics and Decision Making (2021)
7. Machine Learning-Based Clinical Decision Support System for Automatic Diagnosis of Covid-19 Based on Clinical Data, Journal of Biostatistics and Epidemiology (2022)
8. A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset, IEEE Access (2020)
9. Machine Learning-Based Prediction of 1-Year Mortality in Hypertensive Patients Undergoing Coronary Revascularization Surgery, Clinical Cardiology (2023)
10. Prediction of Breast Cancer Using Machine Learning Approaches, Journal of Biomedical Physics and Engineering (2022)
11. Prediction of the Fatal Acute Complications of Myocardial Infarction Via Machine Learning Algorithms, Journal of Tehran University Heart Center (2023)
12. Machine Learning Approaches to Predict the Need For Intensive Care Unit Admission Among Iranian Covid-19 Patients Based on Icd-10: A Cross-Sectional Study, Health Science Reports (2024)
13. Machine Learning Prediction of One-Year Mortality After Percutaneous Coronary Intervention in Acute Coronary Syndrome Patients, International Journal of Cardiology (2024)
14. Machine Learning Algorithms for Predicting Mortality After Coronary Artery Bypass Grafting, Frontiers in Cardiovascular Medicine (2022)
15. An Ensemble Data Mining Approach to Discover Medical Patterns and Provide a System to Predict the Mortality in the Icu of Cardiac Surgery Based on Stacking Machine Learning Method, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (2023)
16. Enhancing Machine Learning Performance in Cardiac Surgery Icu: Hyperparameter Optimization With Metaheuristic Algorithm, PLoS ONE (2025)
17. Prediction of Mortality in Patients Admitted to Intensive Care Units, a Comparison of Three Data Mining Techniques: A Brief Report., Tehran University Medical Journal (2021)
18. Comparison of Different Machine Learning Algorithms to Classify Patients Suspected of Having Sepsis Infection in the Intensive Care Unit, Informatics in Medicine Unlocked (2023)
19. Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer, Mathematical Problems in Engineering (2022)
20. Predicting the Mortality of Patients With Covid-19: A Machine Learning Approach, Health Science Reports (2023)