Research On The Law Of Tibetan Medicine Prescriptions For Common Diseases On The Plateau Based On Data Mining

Tibetan medicine is a treasure of medical culture that has been handed down by Chinese Tibetan people through years of experience.Its methods of treatment and the laws of medication have their own unique systems,which makes their treatment of common diseases on the plateau significantly better than other medicines.Because the medicine,treatment of Tibetan medicine are greatly different due to external factors,this study uses data mining algorithms to study the rules of Tibetan medicine in the plateau,and eliminates external factors for Tibetan medicine through scientific methods.Influencing,exploring the rules of medication used in the treatment of Tibetan medicine,and discovering the characteristics of medication used by different diseased groups.This paper studies the clinical data of clinical diagnosis and treatment of chronic atrophic gastritis in common diseases of the plateau through three aspects:(1)Prescription of Tibetan medical treatment data.Through the use of different pre-processing methods,found an effective treatment method that meets the characteristics of Tibetan medical data.Based on the KNN algorithm,the missing data of Tibetan medicine type is completed.At the same time,the association analysis is used to verify the common drug attribute selection method when selecting attributes;2)Study the symptoms of symptomatic medication.The idea of drug atom combination was introduced for the first time.Based on the improved ECLAT algorithm and the improved CBA algorithm,the drugs use of Tibetan medicine was studied,and the “symptom==>medication” decision-making model was established.The prediction accuracy of the model was 76%.(3)Research on Tibetan medicine type medication.Based on the classification of the syndromes after the completion of the classification,based on a variety of classification algorithms to compare experiments,the "disease ==> medication" decision-making model was established,and the prediction accuracy of the model was 76.5%.Finally,based on the improved K-means algorithm for semi-supervised learning,found the characteristics of the group medication.This study proves that after the research on the law of Tibetan medicine based on data mining,the standardization of Tibetan medical drugs is obviously improved,and the methods used in this study are all in line with the characteristics of Tibetan medical data,which can be applied to the Tibetan medicine prescriptions of more common diseases in the plateau.In the study,it will help promote the scientific development of Tibetan medicine.