A Quantitative Method for Pulse Strength Classification Based on Decision Tree

Pulse diagnosis is one of the most important examinations in Traditional Chinese Medicine (TCM). In response to the subjectivity and fuzziness of pulse diagnosis in TCM, quantitative systems or methods are needed to modernize pulse diagnosis. In pulse diagnosis, strength is one of the most difficult factors to recognize. To explore the quantitative recognition of pulse strength, a novel method based on decision tree (DT) is presented. The proposed method is testified by applying it to classify four hundreds pulse signal samples collected from clinic. The results are mostly accord with the expertise, which indicate that the method we proposed is feasible and effective and can identify pulse signals accurately, which can be expected to facilitate the modernization of pulse diagnosis.

[1]  Gary M. Weiss Mining with rarity: a unifying framework , 2004, SKDD.

[2]  Hui-yan Wang,et al.  Investigation on the automatic parameters extraction of pulse signals based on wavelet transform , 2007 .

[3]  Rahul Sukthankar,et al.  Complete Cross-Validation for Nearest Neighbor Classifiers , 2000, ICML.

[4]  Jie Wang,et al.  A Quantitative Diagnostic Method Based on Bayesian Networks in Traditional Chinese Medicine , 2006, ICONIP.

[5]  Lior Rokach,et al.  Top-down induction of decision trees classifiers - a survey , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Ching-Hsiu Chen,et al.  Self-organizing arterial pressure pulse classification using neural networks: theoretical considerations and clinical applicability , 2000, Comput. Biol. Medicine.

[7]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[8]  Ron Kohavi,et al.  Wrappers for feature selection , 1997 .

[9]  Jie Cheng,et al.  Improved Decision Trees: A Generalized Version of ID3 , 1988, ML.

[10]  Pei-Yong Zhang,et al.  A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals , 2008, EURASIP J. Adv. Signal Process..

[11]  R. Barandelaa,et al.  Strategies for learning in class imbalance problems , 2003, Pattern Recognit..

[12]  Hui-yan Wang,et al.  A model for automatic identification of human pulse signals , 2008 .

[13]  Christopher Meek,et al.  Learning Bayesian Networks with Discrete Variables from Data , 1995, KDD.

[14]  Salvatore Ruggieri,et al.  Efficient C4.5 , 2002, IEEE Trans. Knowl. Data Eng..

[15]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[16]  Paul M. Mather,et al.  DECISION TREE BASED CLASSIFICATION OF REMOTELY SENSED DATA , 2001 .

[17]  Huiyan Wang,et al.  A Computerized Diagnostic Model Based on Naive Bayesian Classifier in Traditional Chinese Medicine , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[18]  Huiyan Wang,et al.  A quantitative system for pulse diagnosis in Traditional Chinese Medicine , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[19]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[20]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[21]  Desheng Dash Wu Detecting information technology impact on firm performance using DEA and decision tree , 2006, Int. J. Inf. Technol. Manag..

[22]  Hong-Yeop Song,et al.  A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[24]  David Zhang,et al.  Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter , 2007, Comput. Biol. Medicine.

[25]  Andrew K. C. Wong,et al.  Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.