
This retrospective study aimed to develop a practical tool for predicting the 5-year survival status of laryngeal squamous cell carcinoma (LSCC) patients. Data from two centers, including 150 patients, were used to establish machine learning models. The support vector machine (SVM) algorithm demonstrated the best performance, with an accuracy of 85.0%, sensitivity of 87.5%, specificity of 75.0%, and AUC of 81.2%. The model was translated into an online prediction platform for convenient clinical use. Interpretability analysis highlighted clinical stage as the most crucial feature. This tool provides clinicians with valuable insights for evaluating the prognosis of LSCC.
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