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Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis
Obesity is a prevalent metabolic disease that increases the risk of other diseases, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, and certain cancers. A meta-analysis of 11 randomized sham-controlled trials indicates that acupuncture had adjuvant benefits in improving simpl...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947926/ https://www.ncbi.nlm.nih.gov/pubmed/35341146 http://dx.doi.org/10.1155/2022/7252213 |
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author | Lu, Ping-Hsun Chen, Yu-Yang Tsai, Fu-Ming Liao, Yuan-Ling Huang, Hui-Fen Yu, Wei-Hsuan Kuo, Chan-Yen |
author_facet | Lu, Ping-Hsun Chen, Yu-Yang Tsai, Fu-Ming Liao, Yuan-Ling Huang, Hui-Fen Yu, Wei-Hsuan Kuo, Chan-Yen |
author_sort | Lu, Ping-Hsun |
collection | PubMed |
description | Obesity is a prevalent metabolic disease that increases the risk of other diseases, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, and certain cancers. A meta-analysis of 11 randomized sham-controlled trials indicates that acupuncture had adjuvant benefits in improving simple obesity, and previous studies have reported that acupoint combinations were more useful than single-acupoint therapy. The Apriori algorithm, a data mining-based analysis that finds potential correlations in datasets, is broadly applied in medicine and business. This study, based on the Apriori algorithm-based association rule analysis, found the association rules of acupoints among 11 randomized controlled trials (RCTs). There were 23 acupoints extracted from 11 RCTs. We used Python to calculate the association between acupoints and disease. We found the top 10 frequency acupoints were Extra12, TF4, LI4, LI11, ST25, ST36, ST44, CO4, CO18, and CO1. We investigated the 1118 association rule and found that {LI4, ST36} ≥ {ST44}, {LI4, ST44} ≥ {ST36}, and {ST36, ST44} ≥ {LI4} were the most associated rules in the data. Acupoints, including LI4, ST36, and ST44, are the core acupoint combinations in the treatment of simple obesity. |
format | Online Article Text |
id | pubmed-8947926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89479262022-03-25 Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis Lu, Ping-Hsun Chen, Yu-Yang Tsai, Fu-Ming Liao, Yuan-Ling Huang, Hui-Fen Yu, Wei-Hsuan Kuo, Chan-Yen Evid Based Complement Alternat Med Research Article Obesity is a prevalent metabolic disease that increases the risk of other diseases, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, and certain cancers. A meta-analysis of 11 randomized sham-controlled trials indicates that acupuncture had adjuvant benefits in improving simple obesity, and previous studies have reported that acupoint combinations were more useful than single-acupoint therapy. The Apriori algorithm, a data mining-based analysis that finds potential correlations in datasets, is broadly applied in medicine and business. This study, based on the Apriori algorithm-based association rule analysis, found the association rules of acupoints among 11 randomized controlled trials (RCTs). There were 23 acupoints extracted from 11 RCTs. We used Python to calculate the association between acupoints and disease. We found the top 10 frequency acupoints were Extra12, TF4, LI4, LI11, ST25, ST36, ST44, CO4, CO18, and CO1. We investigated the 1118 association rule and found that {LI4, ST36} ≥ {ST44}, {LI4, ST44} ≥ {ST36}, and {ST36, ST44} ≥ {LI4} were the most associated rules in the data. Acupoints, including LI4, ST36, and ST44, are the core acupoint combinations in the treatment of simple obesity. Hindawi 2022-03-17 /pmc/articles/PMC8947926/ /pubmed/35341146 http://dx.doi.org/10.1155/2022/7252213 Text en Copyright © 2022 Ping-Hsun Lu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lu, Ping-Hsun Chen, Yu-Yang Tsai, Fu-Ming Liao, Yuan-Ling Huang, Hui-Fen Yu, Wei-Hsuan Kuo, Chan-Yen Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis |
title | Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis |
title_full | Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis |
title_fullStr | Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis |
title_full_unstemmed | Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis |
title_short | Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis |
title_sort | combined acupoints for the treatment of patients with obesity: an association rule analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947926/ https://www.ncbi.nlm.nih.gov/pubmed/35341146 http://dx.doi.org/10.1155/2022/7252213 |
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