Using Machine Learning Models to Detect Pancreatic Cancer
Molloy Faculty Mentor
Helen Dang
Presenter Major
Computer Science
Presentation Type
Oral
Location
Hays Theater, Wilbur Arts Building, Molloy University
Start Date
1-5-2026 9:47 AM
End Date
1-5-2026 9:53 AM
Description (Abstract)
In our presentation, we will discuss some key aspects of machine learning/deep learning models. From there, we will present the application of these models and explain how they can be applied to support the early detection of pancreatic cancer. Since this cancer is highly aggressive and develops without symptoms, early diagnosis remains a significant problem. In this research, we will use a publicly available Pancreatic Cancer dataset and apply several different machine learning/deep learning models to conduct experiments on this dataset. Experimental comparative evaluations will be provided for each model. This application will assist and improve the clinical diagnosis of pancreatic cancer by identifying high-risk patients earlier and potentially saving more lives.
Keywords
Pancreatic Cancer(PDAC), Early Detection, Machine Learning, Deep Learning, Comparative Evaluation, Predictive Modeling, Clinical Diagnosis Support
Related Pillar(s)
Service, Study
Using Machine Learning Models to Detect Pancreatic Cancer
Hays Theater, Wilbur Arts Building, Molloy University
In our presentation, we will discuss some key aspects of machine learning/deep learning models. From there, we will present the application of these models and explain how they can be applied to support the early detection of pancreatic cancer. Since this cancer is highly aggressive and develops without symptoms, early diagnosis remains a significant problem. In this research, we will use a publicly available Pancreatic Cancer dataset and apply several different machine learning/deep learning models to conduct experiments on this dataset. Experimental comparative evaluations will be provided for each model. This application will assist and improve the clinical diagnosis of pancreatic cancer by identifying high-risk patients earlier and potentially saving more lives.
