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

ASIAIR > College of Management > Department of Business Administration > Journal Article > Item 310904400/115579

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/115579

Title:	Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification
Authors:	陳興忠;Chen, Hsing-Chung;Damarj, Cahya;Damarjati, Cahya;Trin, Karisma;Putra, Karisma Trinanda;Chen, Han-MI;Chen, Han-MI;Ching-Liang;Hsieh, Ching-Liang;Lin, Hung-Jen;Lin, Hung-Jen;Wu, Mei-Yao;Wu, Mei-Yao;Chin-Sheng, Chin-Sheng C;Chen, Chin-Sheng
Contributors:	資訊電機學院資訊工程學系
Keywords:	Clinical decision support systems；Pulse-Line； Intersection (PLI)e；Xplainability AI (XAI)
Date:	2022-03-01
Issue Date:	2023-03-29 02:49:53 (UTC+0)
Publisher:	亞洲大學
Abstract:	State-of-the-art artificial intelligence (AI) methods are progressively strengthened in Traditional Chinese Medicine (TCM) pulse palpation, aiding physicians to make comprehensive preliminary clinical decisions through non-invasive diagnostics. One of the well-known proven examinations i.e., hesitant pulse wave diagnosis, is a sign that the blood circulation of a person is sluggish. This examination provides a preliminary diagnosis for physiological problems. Modern AI methods such as artificial neural networks achieve better performance than traditional methods; however, the final decision of such examination lacks of interpretability. In clinical situations, patients need an easy-to-understand diagnosis to be provided for selecting appropriate clinical treatment. Therefore, this study presents feature extraction and clinical decision support systems based on Pulse-Line Intersection (PLI) and eXplainability AI (XAI) methods. The pulses were recorded from 46 patients in six different measurement points for six seconds. In addition, a comparison of several AI methods was provided to classify hesitant and normal pulse. The contribution of each feature in the classification process was analyzed by unboxing each predictive intelligence model. The results revealed that all models performed comparably, evaluated using performance matric on the testing data with average F1-score of Logistic Regression, Support Vector Machine, Random Forest, XGBoost, Multi-Layer Perceptron, and Long Short-Term Memory were 0.74, 0.74, 0.74, 0.78, 0.73, and 0.80, respectively. This work suggests that modern AI methods can provide more comprehensive explainability and higher accuracy than traditional method rankings.
Appears in Collections:	[Department of Business Administration] Journal Article

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