NORMA eResearch @NCI Library

An Ensemble Learning Algorithm for ICU Patient Mortality Prediction

Gaffney, Aoife (2021) An Ensemble Learning Algorithm for ICU Patient Mortality Prediction. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (1MB) | Preview


Prediction of patient mortality in Intensive Care Units (ICU) can aid the prevision of timely medical intervention and allocation of vital resources to those patients who are at the greatest risk of dying and for the provision of suitable interventions to save their lives. There is a lack of current research in an accurate, robust and timely solution that can handle complex imbalanced ICU data with significant missing values. This study examines the use of ensemble algorithms to produce reliable results in the prediction of ICU patient mortality by using patient’s medical history data. Four popular single classifiers; Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR) and Support Vector Machines (SVM) and three ensemble classifiers; Light Gradient Boosting Machine (LGBM), Random Forest (RF) and Stacking are implemented in this research. Experiments are conducted with and without feature selection on a test set that handles data imbalance and missing values. The results indicate that the LGBM model without feature selection outperformed the state of the art approaches in terms of accuracy (0.97) and Area Under the Curve (AUC) (0.97). It was found that automated data-driven features selection did not improve the model performance if there was no prior domain knowledge.

Item Type: Thesis (Masters)
Uncontrolled Keywords: ICU; Patient Mortality; Classification; Stacking; LGBM; Ensemble Machine Learning; Feature Selection
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Q Science > Life sciences > Medical sciences
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 25 Nov 2021 17:01
Last Modified: 25 Nov 2021 17:01

Actions (login required)

View Item View Item