基于机器学习的CatBoost模型在预测重症手足口病中的应用

目的 通过机器学习算法，探究CatBoost模型在预测重症手足口病（HFMD）中的应用价值。方法 收集郑州市某医院2014年1月-2017年6月住院部诊治的2 983例HFMD患儿，使用R 3.4.3软件进行数据分析，构建CatBoost模型和其他普通模型，评估CatBoost模型的预测性能。结果 最终构建的CatBoost模型，预测正确率可达87.6%，人工神经网络模型位居第二（83.8%），其他（决策树、支持向量机、logistic回归、贝叶斯网络）模型预测正确率<80%。CatBoost算法模型ROC曲线下面积、灵敏度、特异度均高（分别为0.866、80.80%、92.33%），其中居前3位的预测变量依次为呕吐、肢体抖动和病原学结果。结论 CatBoost模型可以用于预测重症HFMD，相比于其他传统算法，具有较高的预测正确率和诊断价值。

Application of CatBoost model based on machine learning in predicting severe hand-foot-mouth disease

Objective To explore the value of CatBoost model in predicting severe hand-foot-mouth disease (HFMD) by the machine learning algorithm. Methods A total of 2 983 children with HFMD diagnosed and treated in a hospital in Zhengzhou from January 2014 to June 2017 were collected, data were analyzed with R 3.4.3 software, CatBoost model and other common models were constructed, prediction performance of CatBoost model was evaluated. Results The predictive accuracy of the finally constructed CatBoost model was 87.6%, artificial neural network model ranked second (83.8%), other models (decision tree, support vector machine, logistic regression, Bayesian network) had predictive accuracy less than 80%. The area under receiver operating characteristic (ROC) curve, sensitivity, and specificity of CatBoost algorithm model were all high (0.866, 80.80% and 92.33% respectively), the top three predictive variables were vomiting, limb jitter, and pathogenic results. Conclusion CatBoost model can be used to predict severe HFMD, which has higher accuracy and diagnostic value than other traditional algorithms.