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oai:www.ideals.illinois.edu:2142/14710 2023-07-10 col_2142_10761 col_2142_5131 com_2142_10755 com_2142_234 com_2142_5130

Made available in DSpace on 2010-01-06T16:40:06Z (GMT). No. of bitstreams: 8 license.txt: 4075 bytes, checksum: 75c9f88c8c3d53847b2b8300486ad025 (MD5) Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) 1_Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) 2_Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) 3_Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) thesis.tex: 119144 bytes, checksum: 4bcf6a8021ae1e388a2ba2e3bc7db1d1 (MD5) thesis.pdf: 1324498 bytes, checksum: aca09920ecbf08aa6cff0121688f27ca (MD5) Ramachandran_Chandrasekar.pdf: 1324282 bytes, checksum: 723e4d2e6bdf7e8d0a2bfd1903cb1cf9 (MD5) Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by William Ingram (wingram2@illinois.edu) on 2010-01-06T16:40:28Z Item is restricted until 2012-01-06T16:40:25Z Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:14Z Item was in collections: Dissertations and Theses - Computer Science (ID: 587) University of Illinois Dissertations and Theses (ID: 204) No. of bitstreams: 9 license.txt: 4075 bytes, checksum: 75c9f88c8c3d53847b2b8300486ad025 (MD5) Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) 1_Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) 2_Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) 3_Ramachandran_Chandrasekar.tex: 116907 bytes, checksum: 6ff74ee0854d0b449656849027c384a9 (MD5) thesis.tex: 119144 bytes, checksum: 4bcf6a8021ae1e388a2ba2e3bc7db1d1 (MD5) Ramachandran_Chandrasekar.pdf.txt: 101826 bytes, checksum: dd90bcafa4866b790cf82df118f58a4b (MD5) thesis.pdf: 1324498 bytes, checksum: aca09920ecbf08aa6cff0121688f27ca (MD5) Ramachandran_Chandrasekar.pdf: 1324282 bytes, checksum: 723e4d2e6bdf7e8d0a2bfd1903cb1cf9 (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:14Z http://hdl.handle.net/2142/14710 en Copyright 2009 Chandrasekar Ramachandran islet cell transplants medical data mining dimensionality reduction association rule mining A Framework for Knowledge Discovery from Sparse, High-Dimensional Medical Datasets Han, Jiawei Han, Jiawei Ramachandran, Chandrasekar 2010-01-06T16:40:06Z 2010-01-06T16:40:06Z 2012-01-07T11:00:14Z 2010-01-06T16:40:06Z In this work, we describe a comprehensive framework for knowledge discovery from medical records called SDM-Miner. The records are created before, during and after pancreatic islet cell transplantation1 on a group of diabetic patients. The knowledge discovery focuses on selecting the most relevant variables for predicting the outcome of islet cell transplants temporally, and supporting the medical understanding of the variable relationships that would lead to insulin-free outcome of a transplant with machine learning models. The challenges of knowledge discovery lie in the temporally sparse nature of medical records and the large number of variables which make the traditional statistical analyses ineffective. Our approach to overcome the challenges is to combine data-driven computationally intensive modeling with statistical modeling. The framework incorporates this approach during three phases of knowledge discovery including (1) statistical data-preprocessing, (2) pattern search based dimensionality reduction, and (3) association rule based and conditional probability based data-driven modeling. We evaluate the framework by cross validating the models (of machine learning) using prediction errors and uncertainty of rule discovery. In order to demonstrate the novelty of the framework and the improved performance in knowledge discovery, we report results using real and synthetic datasets. Experimental results on synthetic data act as a sanity check in order to verify the effectiveness of our models in the absence of standard test results. The evaluation results show that our framework led to smaller mean error with the decreasing number of variable samples, higher robustness to Gaussian noise, and higher confidence and support of association rules than the previous methods. Furthermore, we evaluate our proposed technique using existing machine learning algorithms using the Weka toolkit and show the improved performance of our work as compared to previous approaches. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2009-11-30T16:36:45Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 thesis.pdf: 1324498 bytes, checksum: aca09920ecbf08aa6cff0121688f27ca (MD5) thesis.tex: 119144 bytes, checksum: 4bcf6a8021ae1e388a2ba2e3bc7db1d1 (MD5) 2009-12 Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. MS:Computer Science -UIUC 10KS0112MS