Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Nature-Inspired Optimization Algorithm and Machine Learning Techniques

Abstract

PDAC is one of the most harmful cancer causes due to late diagnosis, its rapid progression, and an 11% survival rate of 5 years. Current methods for diagnosis are very costly, uncomfortable, and unreliable, However, better and more accurate solutions are needed. This study proposes a diagnostic model using urinary biomarkers and machine learning techniques for early detection. Key urinary biomarkers, including LYVE-1, REG1B, TFF1, and plasma CA19-9 are used with patient data. Particle Swarm Optimization is used here for feature selection and hyperparameter tuning, optimizes the machine learning classifiers like Support Vector Machine, Logistic Regression, and Random Forest. Accuracy, precision, recall, and F1-score are used as evaluation metrics; however, random forest achieves the highest accuracy of 89.83%. This study shows how PDAC detection changes after combining molecular diagnostics with machine learning. Future research could explore the study of hybrid swarm intelligence algorithms and increase the data set to make further enhancements to diagnostic capabilities. This model shows a great step toward a quick and accurate diagnosis of PDAC and improves patient outcomes and survival rates.