Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
12-2023
Committee Chair or Co-Chairs
Mostafa Zahed
Committee Members
JeanMarie Hendrickson, Robert M. Price
Abstract
This thesis investigates protein markers linked to pulmonary embolism risk using proteomics and statistical methods, employing unsupervised and supervised machine learning techniques. The research analyzes existing datasets, identifies significant features, and observes gender differences through MANOVA. Principal Component Analysis reduces variables from 378 to 59, and Random Forest achieves 70% accuracy. These findings contribute to our understanding of pulmonary embolism and may lead to diagnostic biomarkers. MANOVA reveals significant gender differences, and applying proteomics holds promise for clinical practice and research.
Document Type
Thesis - embargo
Recommended Citation
Awuah, Yaa Amankwah, "Proteomics and Machine Learning for Pulmonary Embolism Risk with Protein Markers" (2023). Electronic Theses and Dissertations. Paper 4327. https://dc.etsu.edu/etd/4327
Copyright
Copyright by the authors.