Clustering analysis of cost-effectiveness ratio and construction of AI machine learning predictive model for laparoscopic cholecystectomy
Objective To investigate the cost-effectiveness ratio(CER)of laparoscopic cholecystectomy and construct predictive model using clustering analysis and AI machine learning method.Methods A retrospective analysis was conduc-ted on clinical data from 206 patients who underwent laparoscopic cholecystectomy in Zhongshan People's Hospital.Total hospitalization cost,total hospitalization time,time from admission to surgery,postoperative hospitalization time,preopera-tive analgesic application frequency,and postoperative analgesic application frequency were served as cost-effectiveness ratio indicators,which were divided into three categories:excellent cost-effectiveness ratio,moderate cost-effectiveness ratio,and poor cost-effectiveness ratio.Variables such as CT inflammation,CT lesion condition,CT lesion location,pathological diag-nosis,CA19-9,AFP,preoperative blood glucose,ALB,AST,ALT,PLT,HBG,RBC,WBC,time from CT to surgery,wound pain severity,surgery duration,preoperative blood pressure,CT lesion size,time from admission to surgery,and pre-operative fasting time were used for multi-class logistic regression analysis and machine learning model construction.Models were fitted by classifiers included logistic regression,linear SVM,SVM,decision tree,random forest,and K-nearest neigh-bors.Results Multi-class logistic regression analysis showed that the fitted chi-square value of the model was 156.986,P<0.001.The likelihood ratios of ALB,WBC,CT to surgery time,hospitalization to surgery time,hypertension,kidney dis-ease,and surgical personnel were statistically significant,and P values were 0.001,0.019,0.029,<0.001,0.005,0.027,<0.001,respectively.The overall conformity rate of the model fitting classification was74.8%(i.e.,the accuracy rate was 0.748).The K-fold validation score of multi-layer perceptron model was 0.461,and prediction score was 0.802;the K-fold validation score of logistic regression model was 0.437,and prediction score was 0.726;the K-fold validation score of the SVM model was 0.529,and prediction score was 0.755;the K-fold validation score of decision tree model was 0.462,and prediction score was 0.585;the K-fold validation score of random forest model was 0.529,and prediction score was 0.726;the K-fold validation score of K-nearest neighbors model was 0.388,and prediction score was 0.623.Conclu-sion Machine learning modeling can effectively predict cost-effectiveness indicators in the analysis of the cost-effectiveness ratio of laparoscopic cholecystectomy surgery,which can be used for preoperative evaluation and optimization of medical re-source allocation based on cost-effectiveness ratio.
万方数据
维普
