Based On Data Mining, This Paper Explores The Medication Rules Of Professor You Zhaoling In The Treatment Of Infertility With Decreased Ovarian Reserve Function

Objective:On the basis of traditional teachers and inheritance,through modern data mining technology,using the methods of frequency,clustering,association rules and principal component analysis,this paper explores the regularity of using drugs in the course of clinical diagnosis and treatment of infertility by Professor Youzhaoling,so as to summarize Professor Youzhaoling's experience in using drugs and cl inical thinking,and to explore his academic theory.It will provide ideas and references for future generations to learn,inherit and innovate.Method:In this study,we collected 87 cases of infertility treated by Professor Youzhaoling from January 1,2018 to January 1,2019,and screened them strictly according to exclusion criteria and inclusion criteria.A total of 222 prescriptions of traditional Chinese medicine were involved.Referring to the 9th edition of TCM textbook and the Dictionary of TCM,the name,efficacy,taste,meridian tropism and medicinal properties of TCM were standardized and processed.The data of TCM names were transformed into classification variables.Microsoft Excel 2007 was used to establish Professor Youzhaoling's database of medication for ovarian reserve dysfunction in infertility diagnosis and treatment on Windows 7 platform.SPSS 23.0 was used for frequency and cluster analysis,and IBM SPSS Modeler 18.0 was used for association rule analysis and principal component analysis.The results were analyzed and discussed in combination with modern medicine and traditional Chinese medicine theory.Result:1.General data analysis: The average age of the enrollees was 34.26 + 5.60 years old,the oldest was 45 years old,and the you ngest was 24 years old;the number of visits and treatments of the enrollees was 2 and 3(except the first visit),26 cases were 2,19 cases were 3,26 cases were 25.74% and 28.22% respectively;the most patients were implanted,24 cases were 27.59%.2.Frequency analysis: 87 cases met the criteria,involving 222 prescriptions of traditional Chinese medicine,including 106 kinds of traditional Chinese medicine.There were 3763 frequencies of all drugs,38 of which were more than 10.The top 10 were Dangshen(221 times,frequency 5.87%),Astragalus membranaceus(220 times,frequency 5.85%),Atractylodes macrocephala(220 times,frequency 5.85%),Pueraria(217 times,frequency 5.77%)and Licorice(216 times,respectively.Frequency 5.74%,Panax notoginseng(166 times,frequency 4.41%),yam(153 times,frequency 4.07%),Cuscuta(146 times,frequency 3.88%),orange leaves(145 times,frequency 3.85%)and lotus seeds(142 times,frequency 3.77%)are shown in Table 1.In the analysis of drug efficacy,it was found that 17 kinds of Chinese medicines were involved,Professor Youzhaoling used most of them,including 28 kinds of drugs,totally 1985 times,accounting for 53.1% of the total drugs,followed by antipyretic drugs,including 19 kinds of drugs,totally 437 tim es,accounting for 11.7% of the total drugs;the third was antiepileptic drugs,including 9 kinds of drugs,totally 389 times,accounting for the total drugs.10.4%;the fourth is astringent drugs,including 12 kinds of drugs,a total of 339 times,account ing for 9.06% of the total drug;the fifth is Qi-Regulating drugs,including 5 kinds of drugs,a total of 181 times,accounting for 4.84% of the total drug,while the lowest use of Liver-Calming drugs.In the analysis of medicinal flavor,four kinds of medicinal flavors were found.The sweeteners were the most widely used,totaling 61 flavors,totaling 2996 times,accounting for 51.9%.According to the analysis of meridian attribution,12 meridians were involved,and 34 kinds of traditional Chinese medicines were used into the spleen meridian,totaling 2051 times,accounting for 22.82%.In the analysis of drug properties,it was found that there were four types of drugs involved,and the most commonly used drugs were plain drugs,with 32 flavors,totaling 1485 times,accounting for 39.65%.3.Association rules: IBM SPSS Modeler 18.0 was used to analyze association rules of herbs.Association rules are unsupervised machine learning methods for knowledge discovery,especially for traditional Chinese medicine mining.Apriori algorithm is one of the most influential algorithms for mining frequent itemsets of association rules.Its core is a recursive algorithm based on the idea of two-stage frequency set.This association rule belongs to single-dimensional,single-level and Boolean association rules in classification.Apriori algorithm adopts the iterative method of layer-by-layer search.The algorithm is simple and clear,without complicated theoretical derivation,and easy to implement.The minimum support degree is 60%,the minimum confidence degree is 80%,and the maximum preceding term is 5.The second,third and fourth order association rules of herbal medicine are analyzed respectively.4.Clustering analysis: Clustering analysis refers to the process of grouping the set of physical or abstract objects into multiple classes composed of similar objects.It can discover the internal structure of data and has the characteristics of comprehensiveness and objectivity.In this study,Q-type cluster analysis was carried out by using distance coefficient statistics and combined with the theory of traditional Chinese medicine,clustering analysis of drugs with frequency more than 5 times was carried out,and the following clusters were obtained: Codonopsis pilosula,Astragalus membranaceus,Atractylodes macrocephala,Pueraria,Licorice,raspberry,Yuzhu,Cuscuta,Lycium barbarbarum,Yam,lotus seed,Dendrobium,lily,orange leaf,Leonurus,Angelica,Chuanxiong,Taraxacum,Zihua Ding.Radix Isatidis,Folium Isatidis,Fors ythia suspense and Prunella vulgaris.5.Principal Component Analysis: According to the load coefficient greater than 0.5,the corresponding medicines for each factor were determined,and the following five factors were obtained: Purple Flower Ding,Yam,Prunella vulgaris,Dandelion,Isatis root,Forsythia suspensa,Daqing leaf,Cuscuta chinensis,Dendrobium,Orange leaf,Lycium barbarum,Lotus seed,Lily,Yuzhu,Angelica,Chuanxiong,Maca,Notoginseng flower,Leonurus japonicus;Nansha,platycodon,Radix Scrophulariae,Radix Scrophulariae,Radix Scrophulariae,Rose,Ophiopogon japonicus,Mulberry,Codonopsis pilosula,Vinegar tortoise shell,Rehmannia glutinosa,Atractylodes macrocephala,Astragalus membranaceus;Poria cocos,Radix Salviae Miltiorrhizae,Fe tal Chrysanthemum,Floating wheat,Shouwuteng,Corn Stir-fried Astragalus membranaceus,Calcified Pearl Mother,Stir-fried Jujube Seed,Astragalus membranaceus;Ramie Root,Perilla stem,White spoon,Dendrobium parasitic,Dipsacum,Lotus Leaf,Pueraria Roo t;Huangbaibaibai,Fre.In conclusion:Through this research,Professor Youzhaoling found that in the course of clinical diagnosis and treatment of infertility,the ovarian reserve function declined.He believed that the patients with ovarian reserve function declined mostly based on kidney deficiency,clinical symptoms of heavy kidney,invigorating spleen and calming mind.According to the characteristics of women's uterine diarrhea,according to the stages of menstruation and follicular growth.Buxu drugs are most commonly used in medicine,but there are many kinds of drugs such as heat-clearing,surface-relieving,Qi-regulating,blood-activating and stasis-removing,which reflect the characteristics of Professor You's combination of attack and tonifying.Professor Youyou's prescription is generally peaceful,maintaining the balance of yin and Yang in treatment.Through proper cold and heat compatibility,he can make up for the deficiency,clear without injury,moisturize and dry mutually.Especially,Prof essor Youyou takes warming nest,assisting egg and sperm filling as the main task,and achieves the co-treatment of the vesicle membrane and sperm and blood.This topic uses modern data mining technology to analyze the law of traditional Chinese medicine in the treatment of infertility by Professor Youzhaoling.It preliminarily explores the law of medicine in the preparation of pregnancy for patients with ovarian reserve decline,and provides new ideas for clinical research of inheriting Professor Youzhaoli ng's experience,but there are some shortcomings.