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| 一种基于灵敏度的中医亚健康诊断方法 | |
| 引用本文： | 王翌,戴莹,郭峰,李绍滋. 一种基于灵敏度的中医亚健康诊断方法[J]. 厦门大学学报(自然科学版), 2012, 51(5): 866-872 |
| 作者姓名： | 王翌 戴莹 郭峰 李绍滋 |
| 作者单位： | 1. 厦门大学信息科学与技术学院,福建厦门,361005 2. 日本岩手县立大学软件与信息科学系,岩手县岩手郡020-0193 3. 厦门大学信息科学与技术学院,福建厦门361005;福建省仿脑智能系统重点实验室(厦门大学),福建厦门361005 |
| 基金项目： | 国家自然科学基金项目(60873179);福建省自然科学基金项目(2011J01367);高等学校博士学科点专项科研基金项目(20090121110032);深圳市科技计划项目(JC200903180630A);深圳市科技研发基金项目(ZYB200907110169A) |
| 摘 要： | 提出了一种基于中医理论的人体亚健康自动诊断方法.通过获取中医诊断中常用的人体舌部、眼部、脸部等视觉信息,并结合心理和生理量表,从而实现中医中的望诊和问诊,检测病人的亚健康程度.同时,为了克服中医诊断中存在的主观性、多样性和不确定性,又提出了灵敏度理论作为选择特征和训练数据的标准.实验表明,基于灵敏度的特征选择和数据选择方法能有效的提高亚健康预测的准确率、相关性和剩余方差等性能. |
| 关 键 词： | 亚健康 中医 灵敏度 特征选择 BP神经网络 |
Key Technology of The Sensitivity-based TCM Sub-health Diagnosis |
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| WANG Yi , DAI Ying , GUO Feng , LI Shao-zi. Key Technology of The Sensitivity-based TCM Sub-health Diagnosis[J]. Journal of Xiamen University(Natural Science), 2012, 51(5): 866-872 | |
| Authors: | WANG Yi DAI Ying GUO Feng LI Shao-zi |
| Affiliation: | 1,3(1.School of Information Science and Technology,Xiamen University,Xiamen 361005,China; 2.Faculty of Software and information Science,Iwate Prefectural University,Iwate 020-0193,Japan; 3.Fujian Key Lab of the Brain-like Intelligent Systems(Xiamen University),Xiamen 361005,China) |
| Abstract: | In this paper we propose an approach of predicting individual’s sub-health based on the principle of TCM as a preventive medicine.The object’s vision features like features of tongue,eye and face are extracted for modeling a process of TCM doctor’s diagnosis.Because of the diversity and uncertainty of TCM doctors’ diagnosis,the sensitivity is defined as a criterion to select the training data from the derived features and the diagnosis data given by different TCM doctors for constructing the sub-health inference model.The experiment results show that the sensitivity-based data selection improves the model’s inference performance on the accuracy,correlation and residual variance. |
| Keywords: | sub-health TCM syndrome sensitivity feature selection BP neural network |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |