Name
Title | ||
|---|---|---|
A spider tool-based qualitative analysis of machine learning for wrist pulse analysis |
Abstract | ||
|---|---|---|
Ayurveda and traditional Chinese medicine (TCM) have used wrist pulse analysis for centuries. It is a non-invasive, novel, convenient, and highly successful disease diagnosis technique that analyses the pulse pattern from the patient’s wrist. During the wrist pulse diagnostic, the clinician uses three fingers to palpate the patient’s wrist to analyze the pressure unbalance as an imbalance of force under the finger may be the cause of disease. However, the unsatisfactory results of clinical rating scales pave the path for future research. Usage of radial pulse to analyze and diagnose disease seems to be a complicated process. As a result, early automatic evaluation can improve life quality and longevity. The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) and SPIDER (Sample, Phenomenon of interest, design, evaluation and research type) search methodology schemes were used to find data and relevant studies for this study. Approximately 800 articles were extracted using relevant search phrases. After a step-by-step mapping and removal of various studies, sixty papers were found appropriate for this review. Following the quality assessment of the collected studies, seven inhibitors from the patient's Wrist are identified for analysis, five of which are significant. The pulse features of many machine learning categorization techniques in previous studies are also compared in this review. The term "future views" refers to the research gaps that have been identified. According to the researchers, wrist pulse analysis findings can help practitioners to understand early biomarkers for disease diagnosis, categorization, and quantification. The significance of this technique of disease diagnosis is low-cost and noninvasiveness. It provides an in-depth understanding of different pulse features that affect disease identification and categorization by encapsulating the wrist pulse. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s13721-022-00361-7 | Network Modeling Analysis in Health Informatics and Bioinformatics |
Keywords | DocType | Volume |
Wrist pulse analysis, SPIDER search tool, Machine learning, Disease diagnosis, Classifiers assessment | Journal | 11 |
Issue | ISSN | Citations |
1 | 2192-6662 | 1 |
PageRank | References | Authors |
0.35 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kumar, Sachin | 1 | 1 | 0.35 |
| Veer, Karan | 2 | 1 | 0.35 |
| Kumar, S. | 3 | 1 | 2.38 |