A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Multilayer neural networks and Bayes decision theory
Neural Networks
Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
C-Net: a method for generating non-deterministic and dynamic multivariate decision trees
Knowledge and Information Systems
Machine Learning
Neural Computation
Machine learning for survival analysis: a case study on recurrence of prostate cancer
Artificial Intelligence in Medicine
A Mixture of Experts Network Structure for Breast Cancer Diagnosis
Journal of Medical Systems
Missing data imputation in breast cancer prognosis
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Computers in Biology and Medicine
Information Sciences: an International Journal
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Diagnosis of psychosomatic disorders using radial basis functions network
EHAC'05 Proceedings of the 4th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications
Neural network classification of otoneurological data and its visualization
Computers in Biology and Medicine
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Journal of Medical Systems
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Impact of censoring on learning Bayesian networks in survival modelling
Artificial Intelligence in Medicine
An investigation of neuro-fuzzy systems in psychosomatic disorders
Expert Systems with Applications: An International Journal
Learning Bayesian networks from survival data using weighting censored instances
Journal of Biomedical Informatics
Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis
Journal of Medical Systems
Artificial Intelligence in Medicine
Extended Gaussian kernel version of fuzzy c-means in the problem of data analyzing
Expert Systems with Applications: An International Journal
WBCD breast cancer database classification applying artificial metaplasticity neural network
Expert Systems with Applications: An International Journal
Hybrid ensemble approach for classification
Applied Intelligence
An artificial neural network approach to the classification of inferred intracranial signals
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ML-CIDIM: multiple layers of multiple classifier systems based on CIDIM
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
E-CIDIM: ensemble of CIDIM classifiers
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Uncensoring censored data for machine learning: A likelihood-based approach
Expert Systems with Applications: An International Journal
Induction of decision trees using an internal control of induction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for breast cancer outcome appear to be significant predictors for overall survival, but probably form part of a bigger picture comprising many factors. Survival estimations are currently performed by clinicians using the statistical techniques of survival analysis. In this sense, artificial neural networks are shown to be a powerful tool for analysing datasets where there are complicated non-linear interactions between the input data and the information to be predicted. This paper presents a decision support tool for the prognosis of breast cancer relapse that combines a novel algorithm TDIDT (control of induction by sample division method, CIDIM), to select the most relevant prognostic factors for the accurate prognosis of breast cancer, with a system composed of different neural networks topologies that takes as input the selected variables in order for it to reach good correct classification probability. In addition, a new method for the estimate of Bayes' optimal error using the neural network paradigm is proposed. Clinical-pathological data were obtained from the Medical Oncology Service of the Hospital Cli@?nico Universitario of Malaga, Spain. The results show that the proposed system is an useful tool to be used by clinicians to search through large datasets seeking subtle patterns in prognostic factors, and that may further assist the selection of appropriate adjuvant treatments for the individual patient.