基于迁移成分分析和支持向量机的肝移植并发症预测方法

已有很多机器学习算法能够很好地应对预测分类问题，但这些方法在用于小样本、大特征空间的医疗数据集时存在着预测准确率和F1值不高的问题。为改善肝移植并发症预测的准确率和F1值，提出一种基于迁移成分分析（TCA）和支持向量机（SVM）的肝移植并发症预测分类方法。该方法采用TCA进行特征空间的映射和降维，将源领域和目标领域映射到同一再生核希尔伯特空间，从而实现边缘分布自适应；迁移完成之后在源领域上训练SVM，训练完成后在目标领域上实现并发症的预测分析。在肝移植并发症预测实验中，针对并发症Ⅰ、并发症Ⅱ、并发症Ⅲa、并发症Ⅲb、并发症Ⅳ进行预测，与传统机器学习和渐进式对齐异构域适应（HDA）相比，所提方法的准确率提升了7.8%~42.8%，F1值达到85.0%~99.0%，而传统机器学习和HDA由于正负样本不均衡出现了精确率很高而召回率很低的情况。实验结果表明TCA结合SVM能够有效提高肝移植并发症预测的准确率和F1值。

Prediction method of liver transplantation complications based on transfer component analysis and support vector machine

Many machine learning algorithms can cope well with prediction and classification， but these methods suffer from poor prediction accuracy and F1 score when they are used on medical datasets with small samples and large feature spaces. To improve the accuracy and F1 score of liver transplantation complication prediction， a prediction and classification method of liver transplantation complications based on Transfer Component Analysis （TCA） and Support Vector Machine （SVM） was proposed. In this method， TCA was used for mapping and dimension reduction of the feature space， and the source domain and the target domain were mapped to the same reproducing kernel Hilbert space， thereby achieving the adaptivity of edge distribution. The SVM was trained in the source domain after transferring， and the complications were predicted in the target domain after training. In the liver transplantation complication prediction experiments for complication Ⅰ， complication Ⅱ， complication Ⅲa， complication Ⅲb， and complication Ⅳ， compared with the traditional machine learning and Heterogeneous Domain Adaptation （HDA）， the accuracy of the proposed method was improved by 7.8% to 42.8%， and the F1 score reached 85.0% to 99.0%， while the traditional machine learning and HDA had high accuracy but low recall due to the imbalance of positive and negative samples. Experimental results show that TCA combined with SVM can effectively improve the accuracy and F1 score of liver transplantation complication prediction.