MODELLING EARLY DETECTION OF PROSTATE CANCER

• Author / Creator

  Liu,Zhengjun

• Prostate cancer is one of the most common cancers among men in the world (excluding non-melanoma skin cancers). According to the statistics from the Canadian Cancer Society, it is the third leading cause of death from cancer in men in Canada. In general, prostate cancer is treatable with 5-year survival rate of 99% for early-stage. However, if the cancer has spread to nearby organs, the 5-year survival rate drops to 28%. Unfortunately, 92% of patients are diagnosed at an advanced stage. Early detection is an ongoing challenge for prostate cancer treatment. Existing statistical models (e.g. principal component analysis) are more likely to inform us the statistical relationship between each metabolite. However, they have a poor performance of predicting the early prostate cancer. In order to improve predictions, in this thesis, we developed models using metabolite profile to identify patients who are likely to having prostate cancer. Several predictive methods such as Support Vector Machine (SVM), K-Nearest-Neighbour (KNN), Random Forest, LASSO, and PLS-DA were used.

• Subjects / Keywords

  • Analysis

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-h1kz-gs07

• License

  Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.