Title page for ETD etd-081606-083026

Document Type thesis

Author Name Hayward, John T

URN etd-081606-083026

Title Mining Oncology Data: Knowledge Discovery in Clinical Performance of Cancer Patients

Degree MS

Department Computer Science

Advisors
Professor Carolina Ruiz, Advisor
Professor Murali Mani, Reader
Professor Michael Gennert, Department Head

Keywords
Clinical Performance
Databases
Cancer
oncology
Knowledge Discovery in Databases
data mining

Date of Presentation/Defense 2006-08-24

Availability unrestricted

Abstract
Our goal in this research is twofold: to develop clinical performance databases of cancer patients, and to conduct data mining and machine learning studies on collected patient records. We use these studies to develop models for predicting cancer patient medical outcomes. The clinical database is developed in conjunction with surgeons and oncologists at UMass Memorial Hospital. Aspects of the database design and representation of patient narrative are discussed here. Current predictive model design in medical literature is dominated by linear and logistic regression techniques. We seek
to show that novel machine learning methods can perform as well or better than these traditional techniques.

Our machine learning focus for this thesis is on pancreatic cancer patients. Classification and regression prediction targets include patient survival, wellbeing scores, and disease characteristics. Information research in oncology is often constrained by type variation, missing attributes, high dimensionality, skewed class distribution, and small data sets. We compensate for these difficulties using preprocessing, meta-learning, and other algorithmic methods during data analysis. The predictive accuracy and regression error of various machine learning models are presented as results, as are t-tests
comparing these to the accuracy of traditional regression methods. In most cases, it is shown that the novel machine learning prediction methods offer comparable or superior performance. We conclude with an analysis of results and discussion of future research possibilities.

Files
jhayward.pdf

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Document Type	thesis
Author Name	Hayward, John T
URN	etd-081606-083026
Title	Mining Oncology Data: Knowledge Discovery in Clinical Performance of Cancer Patients
Degree	MS
Department	Computer Science
Advisors	Professor Carolina Ruiz, Advisor Professor Murali Mani, Reader Professor Michael Gennert, Department Head
Keywords	Clinical Performance Databases Cancer oncology Knowledge Discovery in Databases data mining
Date of Presentation/Defense	2006-08-24
Availability	unrestricted
Abstract Our goal in this research is twofold: to develop clinical performance databases of cancer patients, and to conduct data mining and machine learning studies on collected patient records. We use these studies to develop models for predicting cancer patient medical outcomes. The clinical database is developed in conjunction with surgeons and oncologists at UMass Memorial Hospital. Aspects of the database design and representation of patient narrative are discussed here. Current predictive model design in medical literature is dominated by linear and logistic regression techniques. We seek to show that novel machine learning methods can perform as well or better than these traditional techniques. Our machine learning focus for this thesis is on pancreatic cancer patients. Classification and regression prediction targets include patient survival, wellbeing scores, and disease characteristics. Information research in oncology is often constrained by type variation, missing attributes, high dimensionality, skewed class distribution, and small data sets. We compensate for these difficulties using preprocessing, meta-learning, and other algorithmic methods during data analysis. The predictive accuracy and regression error of various machine learning models are presented as results, as are t-tests comparing these to the accuracy of traditional regression methods. In most cases, it is shown that the novel machine learning prediction methods offer comparable or superior performance. We conclude with an analysis of results and discussion of future research possibilities.
Files	jhayward.pdf