Graduation Year
2016
Document Type
Thesis
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Industrial and Management Systems Engineering
Major Professor
Tapas Das, Ph.D.
Co-Major Professor
Shuai Huang, Ph.D.
Committee Member
Jose Zayas Castro, Ph.D.
Committee Member
Lawrence O. Hall, Ph.D.
Committee Member
Dave Morgan, Ph.D.
Keywords
Decision Tree, RuleFit, Machine Learning, Biomarker Identification, Item Response Theory
Abstract
In this dissertation we present rule-based machine learning methods for solving problems with high-dimensional or complex datasets. We are applying decision tree methods on blood-based biomarkers and neuropsychological tests to predict Alzheimer’s disease in its early stages. We are also using tree-based methods to identify disparity in dementia related biomarkers among three female ethnic groups. In another part of this research, we tried to use rule-based methods to identify homogeneous subgroups of subjects who share the same risk patterns out of a heterogeneous population. Finally, we applied a network-based method to reduce the dimensionality of a clinical dataset, while capturing the interaction among variables. The results show that the proposed methods are efficient and easy to use in comparison to the current machine learning methods.
Scholar Commons Citation
Haghighi, Mona, "Rule-based Risk Monitoring Systems for Complex Datasets" (2016). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/6248
