Research on Classification of Tibetan Medical Syndrome in Chronic Atrophic Gastritis
| Autor: | Yuan Zhang, Ping Liu, Lei Zhang, Lu Wang, Xiaolan Zhu, Shiying Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Apriori algorithm
Association rule learning Computer science 02 engineering and technology Machine learning computer.software_genre Time cost lcsh:Technology lcsh:Chemistry 03 medical and health sciences atomic classification association rules relative support 0202 electrical engineering electronic engineering information engineering General Materials Science classification and prediction Pruning (decision trees) Instrumentation lcsh:QH301-705.5 Partial classification 030304 developmental biology Fluid Flow and Transfer Processes 0303 health sciences business.industry lcsh:T Process Chemistry and Technology General Engineering partial classification Tibetan medical syndrome lcsh:QC1-999 Computer Science Applications Constraint (information theory) Statistical classification ComputingMethodologies_PATTERNRECOGNITION Knowledge base lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 020201 artificial intelligence & image processing Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) computer lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 9, Iss 8, p 1664 (2019) Applied Sciences Volume 9 Issue 8 |
| ISSN: | 2076-3417 |
| Popis: | Classification association rules that integrate association rules with classification are playing an important role in data mining. However, the time cost on constructing the classification model, and predicting new instances, will be long, due to the large number of rules generated during the mining of association rules, which also will result in the large system consumption. Therefore, this paper proposed a classification model based on atomic classification association rules, and applied it to construct the classification model of a Tibetan medical syndrome for the common plateau disease called Chronic Atrophic Gastritis. Firstly, introduce the idea of &ldquo relative support&rdquo and use the constraint-based Apriori algorithm to mine the strong atomic classification association rules between symptoms and syndrome, and the knowledge base of Tibetan medical clinics will be constructed. Secondly, build the classification model of the Tibetan medical syndrome after pruning and prioritizing rules, and the idea of &ldquo partial classification&rdquo and &ldquo first easy to post difficult&rdquo strategy are introduced to realize the prediction of this Tibetan medical syndrome. Finally, validate the effectiveness of the classification model, and compare with the CBA algorithm and four traditional classification algorithms. The experimental results showed that the proposed method can realize the construction and classification of the classification model of the Tibetan medical syndrome in a shorter time, with fewer but more understandable rules, while ensuring a higher accuracy with 92.8%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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