An Online Prognostic Application for Melanoma Based on Machine Learning and Statistics

Background: Melanoma is a common cancer that causes a severe socioeco-nomic burden. Patients usually turn to plastic surgeons to determine their prognosis after surgery. Methods: Data from hundreds of thousands of real-world patients were downloaded from the Surveillance, Epidemiology, and End Results database. Nine mainstream machine learning mod-els were applied to predict 5-year survival probability and three survival analysis models for overall survival prediction. Models that outperformed were deployed online. Results: After manual review, 156,154 real-world patients were included. The deep learning model was chosen for predicting the probability of 5-year survival, based on its area under the receiver operating characteristic curve (0.915) and its accuracy (84.8%). The random survival forest model was chosen for predicting overall survival, with a concordance index of 0.894. These models were deployed at www.makea-a-difference.top/melanoma.html as an online calculator with an interactive interface and an explicit outcome for everyone. Conclusions: Users should make decisions based on not only this online prognostic application but also multidimensional information and consult with multidiscipline specialists. (c) 2022 Published by Elsevier Ltd on behalf of British Association of Plastic, Reconstructive and Aesthetic Surgeons.