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首页> 外文会议>International Symposium on Information Technologies in Medicine and Education;ITME 2012 >Pulse graph characteristics analysis and classification of sub health state
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Pulse graph characteristics analysis and classification of sub health state

机译：亚健康状态脉搏图特征分析与分类

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Objective: To study on sub health state pulse graph classification characteristics and data mining classification method. Method: 1275 cases were divided into health and sub-health groups through health assessment by “health assessment questionnaire”(H20.V2009), another 121 disease cases in the control group; classifying the sub-health pulse graph characteristics by using naïve Bayes, support vector machine, decision tree, neural network data mining algorithm methods according to the pulse diagram parameter evaluation. Result: Decision tree algorithm total classification results on health pulse graph for62%, on sub health pulse graph total classification results for81.1%, on disease pulse graph total classification result is 49.1%, the decision tree algorithm of pulse graph total classification results for the 72%, better than the other algorithms. Decision tree algorithm is more suitable for different health states of the pulse index classification research. Conclusion: Decision tree algorithm was the effect optimal method in data mining on health, sub-health, disease pulse graph classification, data mining method facilitated the classification of pulse graph health, sub health and disease.
机译：目的：研究亚健康状态脉搏图分类特征及数据挖掘分类方法。方法：通过“健康评估问卷”（H20.V2009）进行健康评估，将1275例患者分为健康组和亚健康组，对照组121例。根据脉搏图参数评估，采用朴素贝叶斯，支持向量机，决策树，神经网络数据挖掘算法对亚健康脉搏图特征进行分类。结果：健康脉搏图上的决策树算法总分类结果为62％，亚健康脉搏图上的决策树算法总分类结果为81.1％，疾病脉搏图总分类结果为49.1％，脉搏图总分类结果的决策树算法为72％，优于其他算法。决策树算法更适合于不同健康状态下的脉搏指数分类研究。结论：决策树算法是数据挖掘中对健康，亚健康，疾病脉搏图分类的最佳效果方法，数据挖掘方法有助于对脉搏图健康，亚健康和疾病的分类。

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