Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis

Ping Hsun Lu, Yu Yang Chen, Fu Ming Tsai, Yuan Ling Liao, Hui Fen Huang, Wei Hsuan Yu, Chan Yen Kuo

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number7252213
JournalEvidence-based Complementary and Alternative Medicine
Volume2022
DOIs
StatePublished - 2022

Fingerprint

Dive into the research topics of 'Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis'. Together they form a unique fingerprint.

Cite this