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Prognostication for cancer patients is integral for patient counseling and treatment planning, yet providing accurate prediction can be challenging using existing patient-specific clinical indicators and host factors. In this work, we evaluated common machine learning models in predicting head and neck squamous cell carcinoma (HNSCC) patients' overall survival based on demographic, clinical features and host factors. We found random survival forest had best performance among the modelsdoi:10.3390/cancers13184559 pmid:34572786 fatcat:twyt6ywji5dmzja4g7euomnxli