MS (Master of Science)
Date of Award
Committee Chair or Co-Chairs
JeanMarie Hendrickson, Robert M. Price
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.
Thesis - embargo
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 by the authors.
Available for download on Wednesday, January 15, 2025