Why the study?
Do ERP-based KNN classifiers improve the classification accuracy of pulse waveforms in traditional Chinese pulse diagnosis?
Population
2,470 pulse waveforms from patients aged 20 to 60 years old at Harbin Binghua Hospital, consisting of five…
Comparison
ERP-based difference-weighted KNN classifier and… vs Other classification methods including improved…
Design
Other
Authors
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May facilitate quantitative pulse diagnosis; hypothesis-generating and requires prospective clinical validation.
Do ERP-based KNN classifiers improve the classification accuracy of pulse waveforms in traditional Chinese pulse diagnosis?
The proposed ERP-based KNN classifiers (EDKC and GEKC) achieve high accuracy (up to 91.74%) in classifying traditional Chinese pulse waveforms, providing an effective tool for quantitative pulse diagnosis.
Zhang et al. (2010) studied this question.
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