2.50
Hdl Handle:
http://hdl.handle.net/10755/156585
Type:
Presentation
Title:
Applying Data Mining Methods to Classify Smoking Cessation Status
Abstract:
Applying Data Mining Methods to Classify Smoking Cessation Status
Conference Sponsor:Sigma Theta Tau International
Conference Year:2005
Author:Poynton, Mollie R., MSN, APRN, BC
P.I. Institution Name:University of Utah
Title:Assistant Professor (Clinical)
Knowledge Discovery in Databases (KDD) is the application of data mining methods to detect interesting patterns embedded in data. However, the utility of data mining methods for modeling health behavior patterns relevant to nursing, including smoking cessation, has not been established. This pilot study explored the process of applying data mining methods, including backpropagation neural networks (BPNN), to model and classify smoking cessation status using data from the 2001 National Health Interview Survey (NHIS). BPNN algorithms are well suited for modeling complex relationships, such as those expected in a highly dimensional health survey data set. The data set necessitated extensive pre-processing prior to the application of BPNN algorithms. Feature sub-set selection was performed. Automated pattern search, the ôdata miningö step of the KDD process was conducted, using multiple methods including BPNNs. Weights and thresholds were adjusted iteratively to aid pattern discovery. Models created during the KDD process were validated using 10-fold cross-validation, and evaluated using performance metrics. The steps of pre-processing, feature sub-set selection, and data mining were re-visited in the iterative approach characteristic of KDD. This study served as the pilot for a larger study encompassing cancer control data, and establishes the potential to create predictive models of smoking cessation status based on health survey data. Models created using KDD/ data mining methods may hold both scientific and clinical implications. Discovered patterns may be hypothesis generating for future scientific research, and immediately useful in clinical informatics applications, such as decision support systems and mass customization of health behavior interventions.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleApplying Data Mining Methods to Classify Smoking Cessation Statusen_GB
dc.identifier.urihttp://hdl.handle.net/10755/156585-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Applying Data Mining Methods to Classify Smoking Cessation Status</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2005</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Poynton, Mollie R., MSN, APRN, BC</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Utah</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Assistant Professor (Clinical)</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">mollie.poynton@nurs.utah.edu</td></tr><tr><td colspan="2" class="item-abstract">Knowledge Discovery in Databases (KDD) is the application of data mining methods to detect interesting patterns embedded in data. However, the utility of data mining methods for modeling health behavior patterns relevant to nursing, including smoking cessation, has not been established. This pilot study explored the process of applying data mining methods, including backpropagation neural networks (BPNN), to model and classify smoking cessation status using data from the 2001 National Health Interview Survey (NHIS). BPNN algorithms are well suited for modeling complex relationships, such as those expected in a highly dimensional health survey data set. The data set necessitated extensive pre-processing prior to the application of BPNN algorithms. Feature sub-set selection was performed. Automated pattern search, the &ocirc;data mining&ouml; step of the KDD process was conducted, using multiple methods including BPNNs. Weights and thresholds were adjusted iteratively to aid pattern discovery. Models created during the KDD process were validated using 10-fold cross-validation, and evaluated using performance metrics. The steps of pre-processing, feature sub-set selection, and data mining were re-visited in the iterative approach characteristic of KDD. This study served as the pilot for a larger study encompassing cancer control data, and establishes the potential to create predictive models of smoking cessation status based on health survey data. Models created using KDD/ data mining methods may hold both scientific and clinical implications. Discovered patterns may be hypothesis generating for future scientific research, and immediately useful in clinical informatics applications, such as decision support systems and mass customization of health behavior interventions.</td></tr></table>en_GB
dc.date.available2011-10-26T14:55:36Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T14:55:36Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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