用机器学习与贝叶斯神经网络预测甲状腺癌复发，兼顾准确与可解释性。
Differentiated Thyroid Cancer Recurrence Classification Using Machine Learning Models and Bayesian Neural Networks with Varying Priors: A SHAP-Based Interpretation of the Best Performing Model
- 对比11种模型，逻辑回归在精简特征后准确率达96.11%
- 贝叶斯神经网络结合正态先验，特征筛选后准确率达98.70%
- 采用SHAP方法解释模型，适合临床决策支持场景
分化型甲状腺癌（DTC）复发是重大公共卫生问题，需兼具高精度、可解释性与不确定性感知的分类预测模型。本研究基于包含383名患者和16个临床病理变量的数据集，首先使用11种机器学习模型进行建模，支持向量机（SVM）表现最佳，准确率为0.9481。通过Boruta算法进行特征选择后，逻辑回归（LR）模型在简化数据集上达到最高准确率0.9611。为解决传统模型缺乏不确定性量化的问题，引入六种不同先验分布的贝叶斯神经网络（BNN），包括Normal(0,1)、Normal(0,10)、Laplace(0,1)、Cauchy(0,1)、Cauchy(0,2.5)、Horseshoe(1)。其中，采用Normal(0,10)先验的BNN在完整与精简数据集上的准确率分别达0.9740和0.9870，表现最优。
原文摘要 · Abstract (English)
Differentiated thyroid cancer DTC recurrence is a major public health concern, requiring classification and predictive models that are not only accurate but also interpretable and uncertainty aware. This study introduces a comprehensive framework for DTC recurrence classification using a dataset containing 383 patients and 16 clinical and pathological variables. Initially, 11 machine learning ML models were employed using the complete dataset, where the Support Vector Machines SVM model achieved the highest accuracy of 0.9481. To reduce complexity and redundancy, feature selection was carried out using the Boruta algorithm, and the same ML models were applied to the reduced dataset, where it was observed that the Logistic Regression LR model obtained the maximum accuracy of 0.9611. However, these ML models often lack uncertainty quantification, which is critical in clinical decision making. Therefore, to address this limitation, the Bayesian Neural Networks BNN with six varying prior distributions, including Normal 0,1, Normal 0,10, Laplace 0,1, Cauchy 0,1, Cauchy 0,2.5, and Horseshoe 1, were implemented on both the complete and reduced datasets. The BNN model with Normal 0,10 prior distribution exhibited maximum accuracies of 0.9740 and 0.9870 before and after feature selection, respectively.
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