We applied data mining techniques to the study of acupuncture as a treatment for juvenile myopia, with the aim of identifying hidden patterns in the data.
Fifty patients with juvenile myopia were selected and treated with acupuncture, and data mining was used to analyze the effects of treatment and the influence of behavioral variables. Clustering analysis was used to divide myopia patients into two classifications before acupuncture treatment. Artificial neural network BP algorithm was adopted to analyze the roles of different factors in changes in diopters. An association algorithm was used to analyze factors associated with the subjective experience of acupuncture and average diopter.
The two classification results were fully consistent with the understandings of the ophthalmic circles. The duration of using the Internet and watching TV every day was the main factor that affected vision. Acupuncture feelings and therapeutic effect have a strong correlativity. A good or above experience's score of acupuncture could slow the progression of juvenile myopia.
Collecting data from patients with juvenile myopia by using data mining can extract hidden potential rules and knowledge from the research evidence. The decision support can be provided to improve the doctor's clinical acupuncture treatment effects.
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