Application of Logistic Regression and Principal Component Analysis in TCM diagnosis and treatment

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.