中醫(yī)證型關(guān)聯(lián)規(guī)則挖掘

借助患者病理信息,挖掘患者的癥狀與中醫(yī)證型之間的關(guān)聯(lián)關(guān)系,對治療提供依據(jù),挖掘潛在證素步驟：問卷采隼數(shù)據(jù)，形成原始數(shù)據(jù)數(shù)據(jù)預(yù)處理（數(shù)據(jù)清洗、屬性規(guī)約、數(shù)據(jù)交換）對:采用關(guān)聯(lián)規(guī)則算法，調(diào)整參數(shù),訓(xùn)練得到關(guān)系模型結(jié)合業(yè)務(wù),結(jié)果分析『應(yīng)用實際對:采用關(guān)聯(lián)規(guī)則算法，調(diào)整參數(shù),訓(xùn)練得到關(guān)系模型結(jié)合業(yè)務(wù),結(jié)果分析『應(yīng)用實際1、 數(shù)據(jù)獲取1） 擬定調(diào)查問卷表并形成原始指標(biāo)表2） 定義納入標(biāo)準(zhǔn)與排除標(biāo)準(zhǔn)3） 將收集回來的問卷表整理成原始數(shù)據(jù)2、 數(shù)據(jù)預(yù)處理數(shù)據(jù)清洗（1）數(shù)據(jù)清洗：對數(shù)據(jù)進(jìn)行有效性檢宜r整理成為原始數(shù)據(jù)1400———= 125312001000800600 4(X) onn10469300發(fā)放間卷總數(shù)耳收問卷數(shù)有效問卷數(shù) |屬性規(guī)約屬性規(guī)約:將數(shù)據(jù)的73個屬性取其重要屬性,剔除無關(guān)屬性,最終的屬性為：6種證型得分、分期的屬性值患者編號肝氣郁結(jié)證得分熱毒縊結(jié)證得分沖任失調(diào)證猬分氣血兩虛證得分脾胃虛弱證得分肝腎陰虛證得分TNM分期20140001730723h(1817.H4

數(shù)據(jù)變換1）屬性構(gòu)造屬性構(gòu)造：采用證型系數(shù)代昔具體單證型的證養(yǎng)得分，證型系數(shù)二該證型得分/該證型總分肝氣郁結(jié)證型系數(shù)熱毒繪結(jié)證型系數(shù)沖任失調(diào)證型系數(shù)氣血兩虛證型系數(shù)脾胃虛筋證型系數(shù)鼾腎陰虛證型系數(shù)0.1750.6820.1710.5350.4190.4470.30.7730.29303720.4420.1320」0.0910.2930.2790J630.3952）數(shù)據(jù)離散化由于Apriori關(guān)聯(lián)規(guī)則無法處理連續(xù)型數(shù)值變量，需要將原始數(shù)據(jù)離散化,采用聚類算法將各個證型系數(shù)離散化處理，將每個屬酸成4類。袁8?9肝氣郁結(jié)證型系數(shù)商散表范也標(biāo)識肝氣郁結(jié)證型系數(shù)范國范H3內(nèi)元素的個數(shù)AI(0.0.179]244A2(0.179,0.258]355A3(0.258.0.35]278A4(0.35,0.504]53表8「0熱痺蘊結(jié)證型系數(shù)離敵表范加標(biāo)識結(jié)證型系數(shù)范國范EB內(nèi)元素的個數(shù)BI[0,0.151325范18標(biāo)識熱毒勇結(jié)證型系數(shù)范83范囤內(nèi)元素的個數(shù)B2(0.15.0.296]396B3(0.296.0.485]1801)4(0.485,0.78；29表沖任失調(diào)證型系數(shù)離散表范圈標(biāo)識沖任失調(diào)證型系數(shù)范BI范圉內(nèi)元素的個數(shù)CI(0,0.2011296C2(0.201,0.288)393C3(0.2?&0.415]206C4(0.415.0.61135表8?12氣血兩虛證型系數(shù)離散表范圖標(biāo)識氣血兩虛證型系數(shù)范圍范圍內(nèi)元索的個數(shù)D1(0.0.172]283D2(0.172*0.251]37503(0.251,0.357]228D4(0.357,0.552] 144?8-13脾胃虛弱證型系數(shù)禹敵袁范因標(biāo)識脾好慮弱證型系數(shù)范si范！B內(nèi)元素的個越El(0.0.154]285E2(0」54.0256】307E3(0.256.0.375]244E4(0375.0.526194表&44肝腎陰虛證型系釵鷹玻表范K1標(biāo)識肝腎陰虛證5!系數(shù)范IB范圖內(nèi)元轟的個斂FI(0.0.I7S]200F2(0.178.0.261]237F3(0.261.0353J265F4(0353.0.607]228#讀取數(shù)據(jù)datafile<-read?csv(”?/data/data?csv",header=T)#參數(shù)初始化type<-4#數(shù)據(jù)離散化的分組個數(shù)index<-8#TNM分期數(shù)據(jù)所在列typelabel<-cfA*,"B","C",〃D〃，"E","F") #數(shù)據(jù)離散化后的標(biāo)識前綴set.seed(1234) #固定隨機化種子cols<-ncol(datafile[,1:6])#取六種證型列數(shù)rows<-nrow(datafile[,1:6])#行數(shù)disdata<-matrix(NA,rows,cols+1) #初始化#聚類離散化for(iin1:cols){cl<-kmeans(datafile[,i],type,nstart=20) #對單個屬性列進(jìn)行聚類disdataE,iZ〈-paste(typelabel[i],cl$cluster)}disdataL,cols+ <-datafile[,index]disdataE,cols+1J<~paste("H",disdataC,cols+1],seq="”)#導(dǎo)出數(shù)據(jù)colnames(disdata)〈-cC肝氣郁結(jié)證型系數(shù)熱毒蘊結(jié)證型系數(shù)：”沖任失調(diào)證型系數(shù)氣血兩虛證型系數(shù)：〃脾胃虛弱證型系數(shù)：〃肝腎陰虛證型系數(shù)U“TNM分期〃)write?csv(disdata, file="?/tmp/processedfile?csv",quote=F,:row.names=F)3、模型構(gòu)建

閘幣建模參數(shù)閘幣建模參數(shù)library(arules)it讀入數(shù)據(jù)a<-read.csv(z/./data/processedfile.csv，z,header=TRUE)#讀入數(shù)據(jù)trans<-as(a,"transactions")#將數(shù)據(jù)轉(zhuǎn)換為transactions格式inspect(trans[1:5]) #觀測trans數(shù)據(jù)集中前5行數(shù)據(jù)items#調(diào)用Apriori算法rules<-apriori(trans,parameter=list(support=0.06,confidence=0.75))#生成關(guān)聯(lián)規(guī)則rulesrules#顯示rules中關(guān)聯(lián)規(guī)則條數(shù)inspect(rules)#觀測rules中關(guān)聯(lián)規(guī)則>inspect(rules)#sAQjrules中關(guān)聯(lián)刪01lhs rhs supportconfidenceliftcount{沖任共過證坐系