论文标题
通过机器学习模型预测舌癌患者的生存
Predicting Survival of Tongue Cancer Patients by Machine Learning Models
论文作者
论文摘要
舌癌是起源于口腔和喉咙的常见口腔恶性肿瘤。在改善其诊断，治疗和管理方面已经投入了很多努力。手术切除，化学疗法和放射治疗仍然是舌癌的主要治疗方法。患者的存活决定了治疗效果。先前的研究已经根据描述性统计数据确定了某些生存和风险因素，忽略了临床和人口统计学变量之间的复杂，非线性关系。在这项研究中，我们利用五种尖端的机器学习模型和临床数据来预测治疗后舌癌患者的存活。将五倍的交叉验证，自举分析和置换特征的重要性应用于估计和解释模型性能。我们方法确定的预后因素与以前的临床研究一致。我们的方法是准确，可解释的，因此可以作为舌癌治疗和管理中的其他证据。
Tongue cancer is a common oral cavity malignancy that originates in the mouth and throat. Much effort has been invested in improving its diagnosis, treatment, and management. Surgical removal, chemotherapy, and radiation therapy remain the major treatment for tongue cancer. The survival of patients determines the treatment effect. Previous studies have identified certain survival and risk factors based on descriptive statistics, ignoring the complex, nonlinear relationship among clinical and demographic variables. In this study, we utilize five cutting-edge machine learning models and clinical data to predict the survival of tongue cancer patients after treatment. Five-fold cross-validation, bootstrap analysis, and permutation feature importance are applied to estimate and interpret model performance. The prognostic factors identified by our method are consistent with previous clinical studies. Our method is accurate, interpretable, and thus useable as additional evidence in tongue cancer treatment and management.
