The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Genetic Algorithms as a Tool for Restructuring Feature Space Representations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Journal of Artificial Intelligence Research
Mining lung cancer patient data to assess healthcare resource utilization
Expert Systems with Applications: An International Journal
Relabeling algorithm for retrieval of noisy instances and improving prediction quality
Computers in Biology and Medicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Bladder cancer is the fifth most common malignant disease in the United States with an annual incidence of around 63,210 new cases and 13,180 deaths. The cost for providing care for patients with bladder cancer disease is high. Bladder cancer treatment options such as immunotherapy, chemotherapy, radiation therapy, transurethral resection, and cystectomy, are used with varying success rates. In this research, data from a nationwide bacillus Calmette-Gue'rin (BCG) plus interferon-alpha (IFN-@a) immunotherapy clinical trial was considered. Data mining algorithms were used to analyze the effectiveness of immunotherapy treatment and to understand the prominent parameters and their interactions. The extracted knowledge was used to build a patient recognition model for prediction of treatment outcomes. The data was analyzed to understand the impact of various parameters on the treatment outcome. A list of significant parameters such as cumulative tumor size, presence of residual disease, stages of prior bladder cancer, current state of bladder cancer, and the presence of current bladder cancer (T1) is provided. The decision-making approach outlined in the paper supplemented with additional knowledge bases will lead to a comprehensive analytical road map of the BCG/IFN-@a immunotherapy treatment. It will provide individualized guidelines for each stage of the treatment as well as measure the success of the treatment.