He Songping, Li Xiangxi, Peng Fangyu, Liao Jiazhi, Lu Xia, Guo Hui, Tan Xin, Chen Yanyan
Digital Manufacturing Equipment National Engineering Research Center, Huazhong University of Science and Technology, Wuhan, China.
National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan, China.
BMC Med Inform Decis Mak. 2025 Jul 8;25(1):255. doi: 10.1186/s12911-025-03060-1.
Anaemia is a common complication after kidney transplantation, and the haemoglobin concentration is one of the main criteria for identifying anaemia. Moreover, artificial intelligence methods have developed rapidly in recent years, are widely used in the medical field and have achieved good results.
To optimize the process of constructing a clinical prediction model based on machine learning and improve related technologies. A classification prediction model for the haemoglobin concentration after kidney transplantation was constructed.
Real-world data from 854 kidney transplant patients in a Grade A tertiary hospital were retrospectively extracted. An imputation method combining the K-nearest neighbour algorithm and multilayer perceptron was used to fill in missing values in the dataset. Recursive feature elimination and extreme gradient boosting were used to rank and screen the importance of patient features and reduce the dimensionality of the features. Before the classification prediction model was established, the number of classification categories was determined first, and the optimal ideal cluster was approximated by the ideal cluster under each classification number and the similarity between the ideal cluster and the actual cluster. Finally, five kinds of machine learning methods, random forest, extreme gradient boosting, light gradient boosting machine, linear support vector classifier and support vector machine, were used to establish classification prediction models, and error-correcting output codes were used to optimize each model. A classification prediction model for abnormal haemoglobin concentrations after kidney transplantation was constructed, and the prediction effect was verified.
The imputation method combining the K-nearest neighbour algorithm and multilayer perceptron has a better effect on the imputation of missing values than do the commonly used imputation methods. Among the machine learning methods used for modelling, the prediction results of the tree model are improved to a certain degree after the error-correcting output code optimization. The final model with the best effect is optimized extreme gradient boosting, and the prediction accuracies before and after model optimization are 85.98% and 87.22%, respectively.
The accuracy of the machine learning classification prediction model established by the optimized modelling method and process reached 87.22%, which can assist doctors in preoperative risk prediction.
贫血是肾移植术后常见的并发症，血红蛋白浓度是诊断贫血的主要标准之一。此外，近年来人工智能方法发展迅速，在医学领域广泛应用并取得了良好效果。
优化基于机器学习构建临床预测模型的流程并改进相关技术，构建肾移植术后血红蛋白浓度的分类预测模型。
回顾性提取某三级甲等医院854例肾移植患者的真实世界数据。采用K近邻算法与多层感知器相结合的插补方法填补数据集中的缺失值。采用递归特征消除和极端梯度提升对患者特征的重要性进行排序和筛选，降低特征维度。在建立分类预测模型前，先确定分类类别数量，通过各分类数量下的理想聚类以及理想聚类与实际聚类的相似度来逼近最优理想聚类。最后，采用随机森林、极端梯度提升、轻梯度提升机、线性支持向量分类器和支持向量机5种机器学习方法建立分类预测模型，并采用纠错输出码对各模型进行优化。构建肾移植术后血红蛋白浓度异常的分类预测模型并验证其预测效果。
K近邻算法与多层感知器相结合的插补方法对缺失值的插补效果优于常用插补方法。在用于建模的机器学习方法中，经纠错输出码优化后，树模型的预测结果有一定程度的提高。效果最佳的最终模型为优化后的极端梯度提升，模型优化前后的预测准确率分别为85.98%和87.22%。
优化后的建模方法和流程所建立的机器学习分类预测模型准确率达87.22%，可辅助医生进行术前风险预测。
