Tree-based Filtering in Pulse-Line Intersection Method Outputs for An Outlier-tolerant Data Processing

Cahya Damarjati - Department of Information Technology, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia
Karisma Trinanda Putra - Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia
Heri Wijayanto - Department of Information Engineering, Universitas Mataram, Mataram 83115, Indonesia
Hsing-Chung Chen - Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
Toha Ardi Nugraha - Department of Medical Research, China Medical University Hospital, China Medical University Taichung 404, Taiwan

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Pulse palpation is one of the non-invasive patient observations that identify patient conditions based on the shape of the human pulse. The observations have been practiced by Traditional Chinese Medicine (TCM) practitioners since thousands of years ago. The practitioners measure the patient’s arterial pulses in three points of both patient wrists called chun, guan, and chy, then diagnose based on their knowledge and experience. Pulse-Line Intersection (PLI) method extract features of each pulse from the observed pulse wave sequence. PLI is performed by summing the number of intersections between the artificial line and the pulse wave. The method is proven in differentiating between hesitant with moderate pulse waves. As the method implemented in Clinical Decision Support System (CDSS) related to pulse palpation, some outlier data might emerge and affect the measurement result. Thus, outlier filtering is needed to prevent unnecessary prediction processes by machine learning (ML) models inside CDSS. This study proposed an outlier filtering model using a decision tree algorithm. This concept is designed by analyzing pulse features values and the chance of odd values combination. Then inappropriate values are excepted using several rules. Every pulse feature list that did not pass the filtering rule is categorized as outliers and were not included for further process. The proposed model works more efficiently than ML models dealing with outliers since this procedure is unsupervised learning with a small number of parameters. Overall, the proposed filtering method can be used in pulse measurement applications by eliminating outlier data that might decrease the performance of ML models.


Pulse palpation; outlier filtering; decision tree; CDSS.

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