To Explore The Medication Rules Of Traditional Chinese Medicine In The Treatment Of Non-alcoholic Fatty Liver Disease Based On Data Mining

Objective: This project is through the retrieval,screening and summary of the relevant literature on the treatment of non-alcoholic fatty liver disease(NAFLD)with traditional Chinese medicine in the past five years,and through data mining methods,the results are analyzed include the main selected drugs and prescription rules for the treatment of NAFLD with traditional Chinese medicine,clarify the etiology and pathogenesis of NAFLD,which has a positive effect in guiding the clinical prescription The use of drugs.Methods: This study collected and sorted out the literature on the treatment of NAFLD with traditional Chinese medicine published in the China Journals Full-text Database(CNKI)from nearly 5 years,strictly in accordance with the inclusion and exclusion criteria.We selected 351 documents that met the standards and included 351 prescriptions of traditional Chinese medicine.Refer to the ninth edition of "Chinese Materia Medica" and "Dictionary of Chinese Materia Medica" for the names,properties,flavors,meridian and efficacy of standardized treatment,the data of Chinese medicine names are converted into categorical variables.Excel2013 was used for frequency analysis,SPSS 25.0 was used for cluster analysis and factor analysis,and IBM SPSS Modeler 18.0 was used for Apriori algorithm association rule analysis.The results obtained are combined traditional Chinese medicine theory and modern medical analysis and discussion.Results: 1.General data analysis: A total of 351 articles were included in this study,involving a total of 351 Chinese medicine prescriptions,including 72 in 2015,88 in 2016,55 in 2017,56 in 2018,and 80 in 2019.2.Frequency analysis of traditional Chinese medicines: 195 kinds of traditional Chinese medicines are involved,and the total frequency of all traditional Chinese medicines totals 3678 times.Chinese medicines with a frequency of ≥19 times are selected as high-frequency medicines,with a cumulative frequency of 81.67%,and a total of 41 kinds.The top 12 are Hawthorn(225 times,frequency 6.12%),Alisma(215 times,Frequency5.85%),Salvia(202 times,frequency 5.49%),Poria(188 times,frequency5.11%),Bupleurum(180 times,frequency 4.89%),Atractylodes(174 times,frequency 4.73%),Licorice(143 times),Frequency 3.89%),Cassia(136times,frequency 3.7%),tangerine peel(119 times,frequency3.24%),turmeric(107 times,frequency 2.91%),Pinellia(105 times,frequency 2.85%),Citrus aurantium(85 times,frequency 2.31%),the cumulative frequency of these 12 medicines reached 51.09%.In the analysis of drug properties,the most used mildly cold drugs were 30 flavors,totaling938 times,accounting for 25.5%.In the analysis of medicine taste,the most bitter medicines used,with a total of 101 tastes,totaling 2022 times,accounting for 31.76%.In the analysis of drug return,the spleen meridian drugs were used the most,with a total of 75 flavors,2087 times,accounting for 20.35%.In the analysis of drug efficacy.The use of tonic drugs is the most.It contains 34 types of drugs in total,774 times,accounting for21.04% of the total drugs.16 kinds of drugs,a total of 660 times,accounting for 17.94% of the Diuresis and dampness drugs;the third is the blood circulation and blood stasis removing drugs,including 18 drugs,a total of 484 times,accounting for 13.16% of the total drugs;the fourth is the heat-clearing drugs,including 34 kinds of drugs Drugs,a total of 360 times,accounting for 9.79% of the total drugs.3.Cluster analysis: Systematic cluster analysis was performed on 41 kinds of high-frequency drugs.The traditional Chinese medicine is divided into 6 groups.The first group is Atractylodes,Poria,Cassia twig,Tangerine Peel,Pinellia,White Peony,Fructus Aurantii,Bupleurum,Licorice,Ginger,Angelica,Red Peony,Peach Seed,Safflower,Cyperus rot,Chuanxiong;The second group is Astragalus,Codonopsis;The third group is Magnolia,Coptis,Atractylodes,Coix seed;The fourth group is lotus leaf,Gynostemma,Rhubarb,Polygonum,Hawthorn,Cassia,Salvia,Alisma orientalis,Chinese wolfberry,Schisandra,Panax notoginseng;the fifth group is turmeric,Gallus gallus,Polygonum cuspidatum;the sixth group is Yinchen,gardenia,scutellaria,kudzu root,and turmeric.4.Factor analysis: Carry out factor analysis on 41 kinds of high-frequency Chinese medicines,16 common factors are extracted,and the cumulative contribution rate is 62.867%.F1: Hawthorn,Cassia seed,Chinese wolfberry,Salvia miltiorrhiza,Alisma;F2: Citrus aurantium,White peony root,Bupleurum,Cyperus rot,Licorice;F3: Atractylodes,Poria,Cassia twig;F4: Safflower,Peach Seed,Chuanxiong;F5: Chenpi,Pinellia;F6: Turmeric,Pueraria lobata,Angelica,Radix Paeoniae Rubra;F7:Polygonum multiflorum,Rhubarb,F8: Gardenia,Yinchen,Ginger;F9:Turmeric,Polygonum cuspidatum;F10: Scutellaria,Gallus gallus domesticus;F11: Coptis chinensis,Magnolia;F12: Gynostemma,lotus leaf;F13: Cangzhu;F14: Astragalus,Codonopsis;F15: Panax notoginseng,Coix seed;F16: Schisandra.5.Association rules: Analyze association rules for high-frequency drugs,The second-order,third-order,and fourth-order association rules are analyzed for herbal medicines.The results showed that the combinations with the highest percentage of support were Hawthorn,Salvia and Alisma,the probability is 41.60%.Conclusion: Through this study,it is found that the pathogenesis of non-alcoholic fatty liver disease is mainly internal resistance of phlegm and dampness,stagnation of qi and blood stasis,stagnation for a long time to reduce heat,stagnation of qi,blood stasis,and damp-heat are the fundamental pathological factors of NAFLD,and the fundamental treatment is to promote qi.To promote blood stasis,dispel dampness,resolve phlegm and clear away heat.In clinical treatment of NAFLD,traditional Chinese medicine usually adopts the combination of tonic drugs,diuretic and dampening drugs,blood activating and removing blood stasis drugs,and heat-clearing drugs,so that dampness does not injure the yin,blood is not consumed,and the effect of phlegm,blood stasis,turbidity and diarrhea is achieved.