Combined Acupoints for the Treatment of Patients with Obesity: An Association Rule Analysis.
Ping-Hsun LuYu-Yang ChenFu-Ming TsaiYuan-Ling LiaoHui-Fen HuangWei-Hsuan YuChan-Yen KuoPublished in: Evidence-based complementary and alternative medicine : eCAM (2022)
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.
Keyphrases
- cardiovascular disease
- type diabetes
- metabolic syndrome
- insulin resistance
- weight loss
- randomized controlled trial
- ion batteries
- high fat diet induced
- blood pressure
- weight gain
- machine learning
- coronary artery disease
- systematic review
- risk assessment
- early stage
- electronic health record
- mesenchymal stem cells
- open label
- double blind
- bone marrow
- single molecule
- smoking cessation
- phase iii
- rna seq