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

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

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...

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

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