Research on the medication regularity of traditional Chinese medicine for common chronic diseases based on association rules

Chronic diseases are the kind of diseases that cause the most severe disease burden in China and have brought significant challenges to the health of our people. With the increase of its global prevalence, it has become a serious global public health problem. Association rules can be used to mine the high-frequency groups of traditional Chinese medicine treating common chronic diseases and the strong association between them and find valuable information hidden in medical data sets. This study uses the FP-growth algorithm to mine and analyzes the Chinese patent medicine prescriptions for five common chronic diseases. The primary purpose is to use association rule mining technology to mine the hidden patterns in traditional Chinese medicine prescriptions for treating chronic diseases and to provide chronic disease medical personnel and related researchers with the characteristics and laws of traditional Chinese medicine for treating chronic diseases, which has significant theoretical value for further understanding and innovating traditional Chinese medicine treatment methods for chronic diseases.