A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability

• Main Authors: Huang S., Arpaci I., Al-Emran M., K?l?�arslan S., Al-Sharafi M.A.
• Other Authors: 56465178700
• Format: Article
• Published: Springer 2024
• Subjects: Classical machine learning; Deep learning; Demographic and clinical features; Lung cancer; Survival period prediction; Biological organs; Computerized tomography; Decision trees; Deep neural networks; Diseases; Learning algorithms; Learning systems; Population statistics; Supervised learning; Tumors; Cancer patients; Clinical features; Comparative analyzes; Demographic features; Learning techniques; Lung Cancer; Machine-learning; Forecasting

Lung cancer, one of the deadliest forms of cancer, can significantly improve patient survival rates by 60�70% if detected in its early stages. The prediction of lung cancer patient survival has grown to be a popular area of research among medical and computer science experts. This study aims to pred...