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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Proteomics and Machine Learning for Pulmonary Embolism Risk with Protein Markers

Awuah, Yaa Amankwah 01 December 2023 (has links) (PDF)
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.
Proteomics
Dimension Reduction
Random Forest
Features Extraction
Feature Selection
MANOVA
Lawley-Hotelling’s Test
Pillai’s Test
Wilk’s Lambda
Roy’s Largest Root.
Applied Statistics
Biostatistics
Statistical Models

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