A Comprehensive exploration of machine learning in early detection with a focus on lung and pancreatic cancer for revolutionizing cancer diagnostics

In the ever-evolving landscape of healthcare, this research delves into the integration of machine learning (ML) as a crucial tool for early cancer detection, particularly focusing on lung cancer and pancreatic ductal adenocarcinoma (PDAC). By harnessing electronic medical records (EMR) and innovative urine biomarkers, ML algorithms such as K-Nearest Neighbour, decision trees, Random Forest, and Gradient Boosting classifiers are employed to conduct predictive analysis. The study underscores the critical importance of timely cancer identification in improving patient outcomes and treatment efficacy. Notably, it advocates for novel methodologies to address the pressing clinical needs in PDAC diagnosis, thereby contributing to the ongoing evolution of early cancer detection technologies. Through a comprehensive evaluation of various ML techniques, this research offers nuanced insights into their strengths and limitations, serving as a foundation for advancing healthcare diagnostics. Ultimately, this study holds promise in reshaping the approach to cancer diagnostics and treatment, potentially leading to significant improvements in patient care and outcomes.