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Proteomics and Machine Learning for Pulmonary Embolism Risk with Protein Markers

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.

Identiferoai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5864
Date01 December 2023
CreatorsAwuah, Yaa Amankwah
PublisherDigital Commons @ East Tennessee State University
Source SetsEast Tennessee State University
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceElectronic Theses and Dissertations
RightsCopyright by the authors.

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