Pancretic Cancer Prediction Using urinary biomarkers

Pancreatic Ductal Adenocarcinoma (PDAC) is a highly aggressive cancer with a poor prognosis due to late detection. Conventional diagnostic techniques, such as imaging and biopsies, are invasive, expensive, and often lead to delayed diagnosis. This research presents a novel, non-invasive diagnostic approach that utilizes urinary biomarkers—creatinine, LYVE1, REG1B, REG1A, and TFF1—combined with advanced machine learning algorithms for early pancreatic cancer detection. The proposed system employs deep learning models to analyze biomarker patterns, effectively differentiating PDAC from benign pancreatic conditions and healthy individuals. By automating biomarker analysis through artificial intelligence, the system enhances diagnostic precision, reduces false positives and negatives, and accelerates clinical decision-making. Additionally, a feedback-driven learning mechanism is integrated to refine model accuracy over time, ensuring adaptability to new data. This approach not only provides a cost-effective and scalable alternative to traditional diagnostics but also marks a significant step towards personalized and accessible cancer detection solutions.