Xue Weirong, Xu Bing, Wang Hui, Zhu Xiaoxiao, Qin Jiajia, Zhou Guangshuang, Yu Peilin, Li Shengli, Jin Yingliang
School of Public Health, Xuzhou Medical College, Xuzhou, China.
Department of Otorhinolaryngology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Front Physiol. 2025 May 30;16:1589469. doi: 10.3389/fphys.2025.1589469. eCollection 2025.
Budd-Chiari syndrome (BCS) is a rare global condition with high recurrence rates. Existing prognostic scoring models demonstrate limited predictive efficacy for BCS recurrence. This study aims to develop a novel machine learning model based on multiple kernel learning to improve the prediction of 3-year recurrence in BCS patients.
Data were collected from BCS patients admitted to the Affiliated Hospital of Xuzhou Medical University between January 2015 and July 2022. The dataset was divided into training, validation, and test sets in a 6:2:2 ratio. Models were constructed by evaluating all combinations of four kernel functions in the training set. Hyperparameters for each model were optimized using the particle swarm optimization (PSO) algorithm on the validation set. The test set was used to compare kernel function combinations, with the area under the curve (AUC), sensitivity, specificity, and accuracy as evaluation metrics. The optimal model, identified through the best-performing kernel combination, was further compared with three classical machine learning models.
A kernel combination integrating all four basic kernels achieved the highest average AUC (0.831), specificity (0.772), and accuracy (0.780), along with marginally lower but more stable sensitivity (0.795) compared to other combinations. When benchmarked against classical machine learning models, our proposed MKSVRB (Multi-Kernel Support Vector Machine Model for Three-Year Recurrence Prediction of Budd-Chiari Syndrome) demonstrated superior performance. Additionally, it outperformed prior studies addressing similar objectives.
This study identifies risk factors influencing BCS recurrence and validates the MKSVRB model as a significant advancement over existing prediction methods. The model exhibits strong potential for early detection, risk stratification, and recurrence prevention in BCS patients.
布加综合征（BCS）是一种全球罕见的疾病，复发率高。现有的预后评分模型对BCS复发的预测效果有限。本研究旨在开发一种基于多核学习的新型机器学习模型，以改善对BCS患者3年复发的预测。
收集2015年1月至2022年7月在徐州医科大学附属医院收治的BCS患者的数据。数据集按6:2:2的比例分为训练集、验证集和测试集。通过评估训练集中四个核函数的所有组合来构建模型。使用粒子群优化（PSO）算法在验证集上对每个模型的超参数进行优化。测试集用于比较核函数组合，以曲线下面积（AUC）、敏感性、特异性和准确性作为评估指标。通过性能最佳的核组合确定的最优模型，进一步与三种经典机器学习模型进行比较。
整合所有四个基本核的核组合实现了最高的平均AUC（0.831）、特异性（0.772）和准确性（0.780），与其他组合相比，敏感性略低但更稳定（0.795）。与经典机器学习模型进行基准比较时，我们提出的MKSVRB（用于布加综合征三年复发预测的多核支持向量机模型）表现出卓越的性能。此外，它优于先前针对类似目标的研究。
本研究确定了影响BCS复发的危险因素，并验证了MKSVRB模型是对现有预测方法的重大改进。该模型在BCS患者的早期检测、风险分层和复发预防方面具有强大的潜力。
