A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Problems with Mining Medical Data
COMPSAC '00 24th International Computer Software and Applications Conference
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Modeling medical prognosis: survival analysis techniques
Computers and Biomedical Research
Analysis of Breast Cancer Using Data Mining and Statistical Techniques
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining risk patterns in medical data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Boosting Approach to remove Class Label Noise
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Iterative RELIEF for feature weighting
ICML '06 Proceedings of the 23rd international conference on Machine learning
Robust real-time face detection based on cost-sensitive AdaBoost method
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Toward breast cancer survivability prediction models through improving training space
Expert Systems with Applications: An International Journal
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
Lung cancer survival prediction using ensemble data mining on SEER data
Scientific Programming - Biological Knowledge Discovery and Data Mining
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The use of data mining approaches in medical domains is increasing rapidly. This is mainly because the effectiveness of these approaches to classification and prediction systems has improved, particularly in relation to helping medical practitioners in their decision making. This type of research has become important for finding ways to improve patient outcomes, reduce the cost of medicine, and further advance clinical studies. Therefore, in this paper, data pre-processing RELIEF attributes selection, and Modest AdaBoost algorithms, are used to extract knowledge from the breast cancer survival databases in Thailand. The performance of these algorithms is examined by using classification accuracy, sensitivity and specificity, confusion matrix and stratified 10-fold cross-validation method. Computational results showed that Modest AdaBoost outperforms Real and Gentle AdaBoosts.