Machine learning-based prognostic prediction for cutaneous malignant melanoma patient survival

Abstract

BACKGROUND: Cutaneous malignant melanoma (CMM) is a highly malignant tumor that necessitates early diagnosis and precise survival prediction. The development of accurate prognostic models is essential for improving patient survival. RESEARCH DESIGN AND METHODS: This retrospective study analyzed data from 5979 CMM patients in the SEER database (2004-2015), with external validation using the TCGA dataset. Patients were randomly allocated to training and testing sets in a 7:3 ratio. The SMOTE+DeepSurv (DeepSmote) model was compared against seven models, including DeepSurv, XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). Model performance was evaluated using Area Under the Curve (AUC), accuracy, precision, recall, and F1-score. RESULTS: The DeepSmote model demonstrated superior prognostic performance across both SEER and TCGA datasets. On the SEER test set, it achieved an AUC of 0.96, accuracy of 0.95, and F1-score of 0.95 for 1-year prediction. This strong performance was maintained in the external TCGA cohort (AUC: 0.91, accuracy: 0.88, F1-score: 0.87), and consistent superiority was observed for 3- and 5-year predictions, confirming its robustness and generalizability. CONCLUSION: DeepSmote provides an accurate, generalizable prognostic tool for CMM survival prediction, outperforming other models across multiple datasets and evaluation metrics.

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