Key result
The K-Nearest Neighbor (KNN) classifier achieved an average recognition rate of 87.13% for TCM pulse classification, compared to 80.10% with the Fisher linear discriminant (FLD).
Absolute Event Rate: 87.13% vs 80.1%
Pulse waveform velocity and pulse reflection factor can be used as effective features for the classification of TCM pulses, with KNN providing a higher recognition rate than FLD.
Should not yet alter TCM diagnostic practice; leaves open larger validation studies for ML-based pulse classification.
Pulse diagnosis is one of important diagnosis methods in Traditional Chinese Medicine (TCM). Recognition of TCM pulse has received more and more attention in recent years. Extracting proper features is crucial for satisfactory classification. While most of previous methods for feature extraction of TCM pulse have no specific correlation with the mechanism of TCM pulse, a hemodynamics method is used to calculate the pulse waveform velocity (PWV) and pulse reflection factor(Rf), which reflects the principle of TCM pulse diagnosis. Then the Fisher linear discriminant (FLD) and the K-Nearest Neighbor (KNN) algorithm are employed to classify the data and double cross-validation method is used for accuracy assessment. The average recognition rate was 80.10% by using FLD classifier and it was 87.13% by using KNN classifier. It is concluded that the PWV and Rf may be used as the features for the classification of TCM pulses.
No takes yet. Share an insight, caveat, or question.
Guo et al. (2013) studied Traditional Chinese Medicine (TCM) pulse diagnosis. K-Nearest Neighbor (KNN) algorithm vs. Fisher linear discriminant (FLD) was evaluated on Average recognition rate. The K-Nearest Neighbor (KNN) classifier achieved an average recognition rate of 87.13% for TCM pulse classification, compared to 80.10% with the Fisher linear discriminant (FLD).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: