Term of Award
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mathematical Sciences
I. Emil Iacob
Committee Member 1
Committee Member 2
Self-care activities classification poses significant challenges in identifying children’s unique functional abilities and needs within the exceptional children healthcare system. The accuracy of diagnosing a child's self-care problem, such as toileting or dressing, is highly influenced by an occupational therapists’ experience and time constraints. Thus, there is a need for objective means to detect and predict in advance the self-care problems of children with physical and motor disabilities. We use clustering to discover interesting information from self-care problems, perform automatic classification of binary data, and discover outliers. The advantages are twofold: the advancement of knowledge on identifying self-care problems in children and comprehensive experimental results on clustering binary healthcare data. By using various distances and linkage methods, resampling techniques of imbalanced data, and feature selection preprocessing in a clustering framework, we find associations among patients and an Adjusted Rand Index (ARI) of 76.26\%
Lewis, Rachel A., "Data Patterns Discovery Using Unsupervised Learning" (2019). Electronic Theses and Dissertations. 1934.
Research Data and Supplementary Material