基于 ML-kNN 和信息融合的智能中医高血压分类。
Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion.
机构信息
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
出版信息
Evid Based Complement Alternat Med. 2012;2012:837245. doi: 10.1155/2012/837245. Epub 2012 Jun 3.
Abstract
Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in "treatment based on ZHENG differentiation." From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN-a multilabel learning model-is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance.
摘要
高血压是心脑血管疾病的主要病因之一。积累了大量的高血压临床资料，研究高血压的辨证分型是一个重要而有吸引力的课题，因为中医主要是“辨证论治”。从数据挖掘的角度来看，辨证可以建模为一个分类问题。本文采用 ML-kNN 多标签学习模型作为高血压的分类模型，并进行特征级信息融合，以进一步利用所有信息。实验结果表明，ML-kNN 可以很好地对高血压的辨证进行建模，信息融合有助于提高模型的性能。
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