A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

• Published in: Computers in biology and medicine Vol. 135; p. 104624
• Main Authors: Aldraimli, Mahmoud, Soria, Daniele, Grishchuck, Diana, Ingram, Samuel, Lyon, Robert, Mistry, Anil, Oliveira, Jorge, Samuel, Robert, Shelley, Leila E.A., Osman, Sarah, Dwek, Miriam V., Azria, David, Chang-Claude, Jenny, Gutiérrez-Enríquez, Sara, De Santis, Maria Carmen, Rosenstein, Barry S., De Ruysscher, Dirk, Sperk, Elena, Symonds, R. Paul, Stobart, Hilary, Vega, Ana, Veldeman, Liv, Webb, Adam, Talbot, Christopher J., West, Catharine M., Rattay, Tim, Chaussalet, Thierry J.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.08.2021; Elsevier Limited
• Subjects: Acute toxicity; Algorithms; Breast; Breast cancer; Classification; Data science; Datasets; Desquamation; Early toxicities; Health services; Imbalanced learning; Internal Medicine; Learning algorithms; Machine learning; Mathematical models; Medical research; Medical treatment; Meta-learning; Minority & ethnic groups; Modelling; Other; Patients; Physicists; Prediction models; Radiation therapy; Radiotherapy; REQUITE; Sampling techniques; Side effects; SMOTE; Statistical inference; Subject specialists; Toxicity; Variables; United Kingdom > UK; SMOTE; Desquamation; Imbalanced learning; Meta-learning; Classification; Machine learning; REQUITE; Early toxicities; Radiotherapy

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to...