Study Of Prestigious Traditional Chinese Physicians’ Medication Rules Based On Machine Learning And Data Mining

As we know,traditional Chinese medicine(TCM)has its unique advantage in relieving exercise-induced fatigue and strengthening sporting abilities,which makes TCM gaining more and more importance in terms of prevention and treatment of delayed onset muscle soreness(DOMS).As the significant component of contemporary medical system,traditional Chinese medicine(TCM)is critical to many aspects of medical care,health care and prevention.It not only is an effective medical means,but also carries Chinese long-standing cultural connections.The prestigious traditional Chinese physicians,the successors of TCM and pioneers of contemporary Chinese medicine,represent the top level of the TCM development.Therefore,to study and conclude the experience of TCM diagnosis and treatment are conducive to summarizing the most essential experience of TCM,so as to enhance the progress of clinical diagnosis and treatment of TCM,and facilitate the inheritance as well as promotion of TCM experience and knowledge.However,the current summary of the medication and compatibility by prestigious traditional Chinese physicians is still in a relatively basic stage.That is to say,the excavation of medication rule is relatively simple and the methods are inappropriate.Based on collating the prescriptions and diagnosis data of many prestigious traditional Chinese physicians,this paper explores their diagnosis experience by comprehensively using various data mining and machine learning methods.According to the data characteristics of prestigious traditional Chinese physicians and the problems existing in the analysis,the adaptive improvement is implemented on multiple algorithms.Firstly,by means of the supervised data mining methods,such as density clustering,improved double clustering algorithm,fuzzy clustering and fuzzy association,this paper excavates the medication rules of prestigious traditional Chinese physicians from perspectives drug properties,drug components and drug dosage.Among them,the density clustering algorithm extracts the drug categories to summarize the similarities as well as differences between prestigious traditional Chinese physicians’ medication and contemporary clinic medication.Based on improving the double clustering algorithm,including adding penalty items to the original algorithm formula and introducing punishment matrix,the drug dose sub-matrix containing key component rules is realized in a large amount of prescription information,which provides condition for fu rther analyzing the medication rules of prestigious traditional Chinese physicians.In addition,the fuzzy analysis processing flow including fuzzy clustering and fuzzy correlation is constructed,and the steps of fuzzy aggregation algorithm are improved,so that the output is in accordance with the membership matrix of drug dosage characteristics.Moreover,the drug dosage association rules contained in the prescription are excavated in a way better consistent with the thinking of TCM.Secondly,a variety of supervised feature engineering methods,such as random forest embedded model and winding model,are applied to analyze and study the relationship between prestigious traditional Chinese physicians’ medication as well as dosage and patients’ pathogenesis.On the premise of the generated confrontation network model and the detailed analysis of prestigious traditional Chinese physicians’ prescription,the in-depth network structure is improved,such as adding label attribute to the network input,and additionally discriminating the category characteristics of the input data in the network discrimination.Finally,the prestigious traditional Chinese physicians’ medication rules corresponding different pathogeneses are concluded.