A new model for hospitalization expenses of Gastric cancer based on clustering and support vector machine

ZHOU Tao; LU Hui-ling; WANG Wen-wen; WANG Hui-qun

doi:10.7641/CTA.2017.60545

Abstract：A new modeling method based on clustering and support vector machine (SVM) is proposed to simplify cate-gory labels complexity for the hospitalization expenses of gastric cancer patients and overcome the limitation of traditional cost modeling techniques, thereby providing some theoretical evidence to control and predict hospitalization expenses of gastric cancer patients. 1583 cases of gastric cancer patients in a certain tertiary general hospital of Ningxia from 2009 to 2011 were collected as samples. Total hospitalization expenses were clustered by years using K-means to obtain category labels, SVM was used to forecast and analyze the influencing factors of hospitalization expenses. The classification ac-curacy was used as indexes to evaluate the predicting effect. The experiment result show that hospitalization expenses of gastric cancer patients were increased year by year, and western drugs accounted for most of the hospital expenses(53.74％). The influencing factors of the cost of hospitalization were treatment outcome, surgery, admission situation, hospitalization time, ages and marital status, in which prognosis and surgery were the most important influences. The experimental re-sults showed that the clustering accuracy of K-means by year was increased by 13.13％compared to only by distribution characteristics. The gauss kernel function-based SVM was the most stable model, with a classification accuracy rate of 92.11％when the penalty factor C and parameterγwere set to be 10 and 1, respectively. The method clustered by year was more reasonable to get category labels, and it was effective to combine clustering and SVM to forecast the hospitalization expenses.