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Analyzing Intraductal Papillary Mucinous Neoplasms Using Artificial Neural Network Methodologic Triangulation

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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)

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.

Suggested Citation

  • Steven Walczak & Jennifer B. Permuth & Vic Velanovich, 2019. "Analyzing Intraductal Papillary Mucinous Neoplasms Using Artificial Neural Network Methodologic Triangulation," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 14(4), pages 21-32, October.
    • Handle: RePEc:igg:jhisi0:v:14:y:2019:i:4:p:21-32
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