Graduation Year
2019
Document Type
Thesis
Degree
M.A.
Degree Name
Master of Arts (M.A.)
Degree Granting Department
Mathematics and Statistics
Major Professor
Lu Lu, Ph.D.
Committee Member
Mingyang Li, Ph.D.
Committee Member
Dymtro Savchuk, Ph.D.
Keywords
Gradient Boosting, Oncology, Survival Analysis, statistics, classification, machine learning
Abstract
Cancer is one of the most deadly diseases that the world has been fighting against over decades. An enormous number of research has been conducted, via a wide scale of approaches, raging from genetic analysis to mathematical modeling. Survival analysis is a well-performed methodology frequently used to estimate the survival probability of a patient. Although there has been a large number of methods for survival analysis, efficient exploration of a high-dimensional feature space has been challenging due to its computational cost and complexity. This thesis adapts the component-wise gradient boosting algorithms for cancer survival analysis, and also proposes a new gradient boosting algorithm based on optimizing the Brier’s score. The new method is illustrated with the analysis of the microarray data of diffuse large B-cell lymphoma (DLBCL). The new gradient boosting approach not only has identified similar important biomarkers as previous statistical studies on the same data set, but also offers more insights and gained understanding on medical aspects. In addition, the performance of the new method is demonstrated through a simulation study and compared with a variety of statistical and machine learning methods.
Scholar Commons Citation
Nguyen, Nam Phuong, "Gradient Boosting for Survival Analysis with Applications in Oncology" (2020). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/8062
