Graduate Project
 

Using Machine Learning Models and SVD Dimension Reduction to Predict Cancer Outcomes

Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_projects/8623j624w

Descriptions

Attribute NameValues
Creator
Abstract
  • Machine learning models are powerful tools which may aid in the prediction of survival outcomes of cancer patients. This study evaluated eight classification models and eight regression models on their ability to predict survival outcomes on breast cancer and prostate cancer data sets from the SEER database. The most accurate models, based on the fraction of correct predictions, were found to be the Gradient Boosting models for both classification and regression. To maximize accuracy, the hyperparameters of these two models were optimized. The computational time to train the models and make predictions was evaluated. Further improvements on the models, specifically the Linear Regression model, using SVD dimension reduction and low-rank approximations successfully decreased computational time. The results of this study support the development of future predictive models for cancer outcomes, particularly in the extension of the dimension reduction technique. Successful predictive models would benefit the tens of millions of cancer patients and medical professionals worldwide.
License
Resource Type
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Rights Statement
Publisher
Peer Reviewed
Language

Relationships

Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
file details FinalMastersPaper.pdf 2023-03-01 Public Download