Analysis of TCM Diagnosis and Treatment of Thyroid Diseases Based on Data Mining

Thyroid disease is the most common and frequently occurring endocrine disease. In recent years, the incidence rate of thyroid diseases has increased in recent years due to the gradual change of the ecological environment and the gradual popularization of iodine supplements for iodine deficiency disorders. At present, the prevalence of hyperthyroidism is 1.3%, hypothyroidism is 6.5%, thyroid nodules are 18.6%, and 2%-5% of thyroid nodules are thyroid cancer. Thyroid disease has become a high incidence and common disease in modern society. The number of thyroid disease patients in China has become the second largest disease after hypertension. Thyroid disease belongs to gall disease of traditional Chinese medicine. Compared with western medicine, traditional Chinese medicine has certain advantages in the treatment of gall disease. Compared with western medicine, traditional Chinese medicine can better improve clinical symptoms and reduce toxicity. In this paper, Stata and python were used to analyze and clean the data of TCM diagnosis thyroid medical records. Principal component analysis and factor analysis were used to analyze and clean the symptoms and pathogenesis of the medical record data. The data in the prescription were quantified. The feature selection of the data set was performed by principal component analysis to reduce the data dimension. In the process of treatment, different kinds of prescriptions will be used. Association analysis algorithm in data mining is used to mine the association rules of prescription medication and complications of thyroid disease, which provides a reference for clinical treatment drug selection and disease prevention of traditional Chinese medicine. Logistic regression was used to establish the prediction model of traditional Chinese medicine diagnosis prescription of thyroid by using the clinical data of traditional Chinese medicine.