Lung Cancer Surgical Diagnosis with Data Science

Abstract

Lung cancer remains one of the leading causes of cancer-related deaths worldwide. Early and accurate diagnosis significantly enhances the effectiveness of treatment strategies, including surgical intervention. Traditional diagnostic approaches often rely on imaging, biopsies, and histopathological examinations, which, although effective, can be time-consuming and prone to human error. With the advent of data science, particularly machine learning and big data analytics, the surgical diagnosis of lung cancer has witnessed transformative advancements. This paper explores the integration of data science in surgical diagnosis, focusing on the methodologies, tools, and results obtained through numerical simulations and case studies. The study highlights how predictive modeling, data preprocessing, and advanced imaging analytics contribute to more accurate and timely diagnoses, ultimately improving surgical outcomes.