偏头痛作为一种常见疾病，其患者基数庞大、发作率高，深刻影响患者的日常生活。随着人工智能技术的不断发展，机器学习在疾病分类、预测等领域应用发展迅猛且效果良好。现借助机器学习技术提出一种针对偏头痛病症的分类模型。为处理数据不平衡问题，将合成少数类过采样技术（synthetic minority oversampling technique, SMOTE）与Tomek links方法相结合进行数据处理，并利用传统机器学习方法和类别型特征提升（categorical boosting, CatBoost）、轻量级梯度提升机算法（light gradient boosting machine, LightGBM）、梯度提升树（gradient boosting decision trees, GBDT）、Stacking等集成学习方法进行训练并完成测试。通过贝叶斯优化方法进行超参数调优，对比多种模型性能，最终的结果表明，以CatBoost、LightGBM、GBDT三个模型作为Stacking基学习器的模型表现最佳，其预测准确率可以达到98.84%。这一发现表明该模型在偏头痛病症分型中具有卓越的性能，同时为改善偏头痛病症分型的精确性和可靠性提供了有效的方法。

As a common disease, migraine has a large patient base and a high incidence rate, which deeply affects the daily life of patients. With the continuous development of artificial intelligence technology, machine learning has developed rapidly and achieved good results in the fields of disease classification and prediction. Now, with the help of machine learning technology, a classification model for migraine symptoms was proposed. To address the issue of data imbalance, SMOTE (synthetic minority oversampling technique) was combined with the Tomek links method for data processing. Traditional machine learning methods and ensemble learning methods such as CatBoost(categorical boosting), LightGBM(lightweight gradient boosting machine), GBDT(gradient boosting decision trees), Stacking were used for training and testing. By using Bayesian optimization method for hyperparameter tuning and comparing the performance of multiple models, the final results show that the model using CatBoost, LightGBM, and GBDT as Stacking based learners performs the best, with a prediction accuracy of 98.84%. This discovery indicates that the model has excellent performance in migraine classification, and provides an effective method for improving the accuracy and reliability of migraine classification.