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Analyzing Intraductal Papillary Mucinous Neoplasms Using Artificial Neural Network Methodologic Triangulation
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Author(s): Steven Walczak (School of Information, University of South Florida, Tampa, USA), Jennifer B. Permuth (Departments of Cancer Epidemiology and Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and R, Tampa, USA)and Vic Velanovich (Department of Surgery, College of Medicine, University of South Florida, Tampa, USA)
Copyright: 2022
Pages: 14
Source title:
Research Anthology on Artificial Neural Network Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-2408-7.ch040
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Abstract
Intraductal papillary mucinous neoplasms (IPMN) are a type of mucinous pancreatic cyst. IPMN have been shown to be pre-malignant precursors to pancreatic cancer, which has an extremely high mortality rate with average survival less than 1 year. The purpose of this analysis is to utilize methodological triangulation using artificial neural networks and regression to examine the impact and effectiveness of a collection of variables believed to be predictive of malignant IPMN pathology. Results indicate that the triangulation is effective in both finding a new predictive variable and possibly reducing the number of variables needed for predicting if an IPMN is malignant or benign.
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