Analyzing Intraductal Papillary Mucinous Neoplasms Using Artificial Neural Network Methodologic Triangulation

Steven Walczak, Jennifer B. Permuth and Vic Velanovich
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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
Vic Velanovich: Department of Surgery, College of Medicine, University of South Florida, Tampa, USA

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2019, vol. 14, issue 4, 21-32

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jhisi0:v:14:y:2019:i:4:p:21-32

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