Date of Submission
Fall 2023
Supervisor
Dr. Tariq Mahmood, Professor, Department of Computer Science, School of Mathematics and Computer Science (SMCS), Institute of Business Administration (IBA), Karachi
Committee Member 1
Dr. Rizwan Ahmed Khan, Professor and Dean, Department of Computer Science, Salim Habib University, Karachi, Pakistan.
Committee Member 2
Dr. Shahid Hussain, Associate Professor and Chairperson, Department of Computer Science, School of Mathematics and Computer Science (SMCS), Institute of Business Administration (IBA), Karachi
Degree
Master of Science in Data Science
Department
Department of Computer Science
Faculty/ School
School of Mathematics and Computer Science (SMCS)
Keywords
Mortality Prediction, Deep Learning, Ensemble, Machine Learning, Stacking
Abstract
PICU mortality is a critical issue, and its prediction is essential. In this thesis, our primary objective was to construct a mortality prediction framework for patients in the PICU using deep learning ensemble methods, following extensive experimentation on high-frequency vital signs and laboratory test data. The proposed framework, named PEDICTOR, is a unique and novel design specifically tailored to PICU patients in the tertiary care hospital of Aga Khan University (AKU) in Pakistan. We developed a novel feature selection approach that incorporates expert domain knowledge and sets it apart from other methods. Another distinctive feature of PEDICTOR is that it is trained on specific age-based groups of pediatric patients and provides predictions based on the age group in which the patient lies. To address the problem of class imbalance in the data, we incorporated a Cluster-Based Synthetic Minority Oversampling (SMOTE) technique. After rigorously testing various Machine Learning (ML) and Deep Learning (DL) models, we experimented with various stacker combinations involving ML and DL models such that our PEDICTOR framework contains the optimal stacker model. The PEDICTOR framework provided an overall mortality prediction with an F1-score in the range of (0.441–0.740) for expired patients and an AUCROC in the range of (0.846–0.932) for PICU patients, thereby enabling doctors to save lives and hospitals to manage their resources efficiently.
Document Type
Restricted Access
Submission Type
Thesis
Recommended Citation
Ghias, M. (2023). A Framework of Mortality Prediction of PICU Patients using Ensemble and Deep Learning Methods (Unpublished graduate thesis). Retrieved from https://ir.iba.edu.pk/etd-ms-ds/1
