Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification

Highlights：

• In this paper, the pulse feature extraction namely pulse-line intersection method could generate statistically highly significant 11 features for distinguishing types of pulse.

• Moreover, SHAP (SHapley additive exPlanations) adopted in this paper as part of predictive intelligence component become a proof of the importance of each feature to predict hesitant pulse wave.

• The performance of long short-term memory (LSTM) overcome other prediction models i.e., LR, SVM, XGBoost, random Forest, and M in hesitant pulse wave prediction.

• This manuscript provides image preprocessing to convert pulse image into pulse wave data.

摘要

•In this paper, the pulse feature extraction namely pulse-line intersection method could generate statistically highly significant 11 features for distinguishing types of pulse.•Moreover, SHAP (SHapley additive exPlanations) adopted in this paper as part of predictive intelligence component become a proof of the importance of each feature to predict hesitant pulse wave.•The performance of long short-term memory (LSTM) overcome other prediction models i.e., LR, SVM, XGBoost, random Forest, and M in hesitant pulse wave prediction.•This manuscript provides image preprocessing to convert pulse image into pulse wave data.

论文关键词：CDSS,Clinical decision support systems,PLI,Pulse-Line Intersection,XAI,eXplainability AI,Clinical decision support systems,Pulse-Line Intersection (PLI),eXplainability AI (XAI)

论文评审过程：Received 1 November 2021, Revised 9 December 2021, Accepted 21 December 2021, Available online 10 January 2022, Version of Record 10 January 2022.