The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.

多发性硬化症 (MS) 患者的残疾累积率各不相同。机器学习技术可能提供更强大的手段来预测多发性硬化症患者的病程。在我们的研究中，来自布莱根妇女医院 MS 综合纵向调查（CLIMB 研究）的 724 名患者和来自加州大学旧金山分校 EPIC 数据集的 400 名患者被纳入分析。主要结局是基线访视后长达 5 年内扩展残疾状态量表( EDSS )增加≥ 1.5（恶化）或不增加（非恶化）。使用 CLIMB 数据集结合患者前 2 年的临床和 MRI 纵向观察建立分类模型，并使用 EPIC 数据集进一步验证。我们比较了三种流行的机器学习算法（ SVM、逻辑回归和随机森林）和三种集成学习方法（XGBoost、LightGBM和元学习器L）的性能。建立了一个“阈值”来权衡两个类别之间的表现。识别并比较不同模型的预测特征。疾病发作后不久，机器学习模型在 CLIMB 和 EPIC 数据集上的 AUC 分数分别为 0.79 和 0.83。与独立算法相比，集成学习方法更有效、更稳健。两个集成模型 XGBoost 和 LightGBM 优于我们研究中评估的其他四个模型。在评估的变量中，EDSS、锥体功能和动态指数是预测 MS 病程的最常见预测因子。机器学习技术，特别是集成方法，可以提高多发性硬化症病程预测的准确性。