부산대학교 도서관

Objective: The purpose of the study was to investigate the use of artificial intelligence methods and data mining technology for predicting cervical cancer survivability. The 3 models of artificial neural network, decision tree and logistic regression were investigated and their accuracy values for predicting cervical cancer survivability were evaluated. Methods and material: The Surveillance, Epidemiology, and End Results (SEER), a large dataset, was used to develop the 3 prediction models. The 3 models were 2 popular data mining algorithms, which were artificial neural network and decision tree; and 1 common statistical model, which was logistic regression. The 10-fold cross-validation analysis also measured the unbiased estimation of 3 prediction results for comparing their performances. Results: The results of accuracy of 3 models were respectively 0.8981 of logistic regression, 0.8930 of decision tree and 0.7776 of artificial neural network. The results of logistic regression were ever 1.0 and 0.9942 accuracy. In 10-fold cross-validation analysis, the standard deviation of accuracy of artificial neural network was 0.0786 and it was the worst one among the 3 prediction models. Conclusions: In this research, artificial neural network performed the model for predicting cervical cancer survivability worse (lowest prediction accuracy and largest variation of accuracy in 10-fold cross-validation analysis) than logistic regression and decision tree.