Predicting Breast Cancer Survivability: Comparison of Five Data Mining Techniques / 대한의료정보학회지

ABSTRACT

OBJECTIVE: Today in United States, about one in eight women have been affected with breast cancer over their lifetime. Up to today, some various prediction models using SEER (Surveillance Epidemiology and End Results) datasets have been proposed in past studies. However, appropriate methods for predicting the 5 years survival rate of breast cancer have not established. In this study, we evaluate those models to predict the survival rate of breast cancer patients. METHODS: Five data mining algorithms (Artificial Neural Network, Naive Bayes , Decision Trees (ID3) and Decision Trees(J48)) besides a most generally used statistical method (Logistic Regression) were used to evaluate the prediction models using a dataset (37,256 follow-up cases from 1992 to 1997). We also used 10-fold cross-validation methods to assess the unbiased estimate of the five prediction models for comparison of performance of each method. RESULTS: The accuracy was 85.8+/-0.2%, 84.3+/-1.4%, 83.9+/-0.2%, 82.3+/-0.2%, 75.1+/-0.2% for the Logistic Regression, Artificial Neural, Naive Bayes, Decision Trees (ID3), Decision Trees(J48), respectively. Although the accuracy of Logistic Regression showed the highest performances, the Decision Trees (J48) was the lowest one. CONCLUSIONS: The accuracy of Logistic Regression was the best performances, on the other hand Decision Trees (J48) was the worst. Artificial Neural Network indicated relatively high performance.