Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Leonard Wesley
Second Advisor
Thomas Austin
Third Advisor
Genya Ishigaki
Keywords
Pancreatic ductal adenocarcinoma, PDAC, Evidential reasoning, Decision making, Predictive modeling, Clinical outcomes, medical diagnosis, Dempster Shafer Theory
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a complex disease with hidden clinical indicators, so a reliable diagnosis of PDAC requires high precision and sophisticated analysis. Traditional probabilistic methods often rely on making unwarranted assumptions or undesirable approximations about probabilistic estimates, limiting their ability to provide the precision needed for correct diagnosis and treatment planning. In contrast, Dempster–Shafer Theory offers a formal framework for integrating uncertain and potentially conflicting evidence. This makes it well-suited for analyzing incomplete and ambiguous data typically associated with PDAC. By employing an evidential reasoning (ER) model based on Dempster-Shafer Theory, this approach systematically combines and evaluates imperfect evidence, eliminating the requirement to make unwarranted assumptions or approximations and enhancing analytical fidelity. This project aims to develop a model to improve prediction accuracy and fidelity and support more reliable diagnostic decisions for PDAC.
Recommended Citation
Nalluri, Surya Teja, "An Evidence-Based Approach to Predicting Pancreatic Ductal Adenocarcinoma" (2025). Master's Projects. 1518.
DOI: https://doi.org/10.31979/etd.a8hr-ks8a
https://scholarworks.sjsu.edu/etd_projects/1518
