Predicting Cervical Cancer Survivability

• Main Authors: Mei-Chiue Lin, 林美雀
• Other Authors: 邱昭彰
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/49322546059528418433

碩士 === 元智大學 === 資訊管理學系 === 97 === Cervical cancer is the most common disease that strikes women in the world. Even though the morbidity and the mortality have been decreasing in recent years, the morbidity rates of cervical cancer are the first leading type and the mortality rates are the eighth of the top ten cancers in Taiwan. Data mining was used in this research including C5.0, CART, SVM and Logistic Regress algorithm to find the effective association rule and build up the model of survivability in cervical cancer. We also used 10-fold cross-validation methods to validate the training data and testing data. This study used data from the National Health Insurance database. Data includes 11,617 persons who have applied catastrophic illness of cervical cancer from 1999 to 2002.The results will respectively be indicated the accuracy of testing data as follows. Decision tree (C5.0) is the best predictor with 80.83% accuracy; Logistic Regression is came out to be the second with 80.47% accuracy ; Decision tree CART came out to be the third with 80.29% accuracy ; Support vector machine came out to be the fourth with 79.93% accuracy. In this study, we found there are many variables including age of patient, bone cancer, lung cancer, liver cancer, non-attendance Cervical Smear, peritonitis and uremia were important factors of the prognosis. We fond the rules and the factors of prognosis which would affect the survivability of the patients of survical cancer in this research. Therefore we can provide reference for patients on treatment or prevent.