The efficacy of machine learning models in lung cancer risk prediction with explainability

• Published in: PloS one Vol. 19; no. 6; p. e0305035
• Main Authors: Pathan, Refat Khan, Shorna, Israt Jahan, Hossain, Md. Sayem, Khandaker, Mayeen Uddin, Almohammed, Huda I., Hamd, Zuhal Y.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.06.2024
• Subjects: Accuracy; Algorithms; Biology and Life Sciences; Cancer; Cancer therapies; Classification; Computer and Information Sciences; Datasets; Engineering and Technology; Health risks; Humans; Learning algorithms; Literature reviews; Lung cancer; Lung diseases; Lung Neoplasms - diagnosis; Machine Learning; Medical personnel; Medical prognosis; Medical research; Medical screening; Medicine and Health Sciences; Neural networks; Oncology, Experimental; Predictions; Research and Analysis Methods; Risk assessment; Risk Assessment - methods; Risk factors; Support vector machines; Three dimensional imaging; Womens health; United Kingdom > UK; United States > US

Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction...