Machine Learning Techniques for Pancreatic Cancer Detection

Pancreatic cancer remains one of the most lethal malignancies, necessitating early detection for improved patient outcomes. This paper presents an overview of machine learning techniques employed in pancreatic cancer detection. Initially, we introduce basic classification algorithms, such as logistic regression and decision trees, followed by a discussion on their limitations. Subsequently, we delve into more advanced techniques like support vector machines and random forests, highlighting their advantages in handling complex data. Finally, we explore deep learning methods, such as convolutional neural networks and recurrent neural networks, showcasing their potential in utilizing diverse data modalities for enhanced accuracy and early diagnosis of pancreatic cancer.