Data mining and knowledge discovery in databases
Communications of the ACM
The data warehouse and data mining
Communications of the ACM
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Expert Systems with Applications: An International Journal
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
A knowledge management system for series-parallel availability optimization and design
Expert Systems with Applications: An International Journal
Design of a two-stage fuzzy classification model
Expert Systems with Applications: An International Journal
Using a hybrid meta-evolutionary rule mining approach as a classification response model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
A new computational intelligence technique based on human group formation
Expert Systems with Applications: An International Journal
A hybrid immune-estimation distribution of algorithm for mining thyroid gland data
Expert Systems with Applications: An International Journal
Novel swarm optimization for mining classification rules on thyroid gland data
Information Sciences: an International Journal
Breast Alert: An On-line Tool for Predicting the Lifetime Risk of Women Breast Cancer
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Using genetic algorithm based knowledge refinement model for dividend policy forecasting
Expert Systems with Applications: An International Journal
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this paper, a genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy. Early many studies of handling the breast cancer diagnostic problems used the statistical related techniques. As the diagnosis of breast cancer is highly nonlinear in nature, it is hard to develop a comprehensive model taking into account all the independent variables using conventional statistical approaches. Recently, numerous studies have demonstrated that neural networks (NNs) are more reliable than the traditional statistical approaches and the dynamic stress method. The usefulness of using NNs have been reported in literatures but the most obstacle is the in the building and using the model in which the classification rules are hard to be realized. We compared our results against a commercial data mining software, and we show experimentally that the proposed rule extraction approach is promising for improving prediction accuracy and enhancing the modeling simplicity. In particular, our approach is capable of extracting rules which can be developed as a computer model for prediction or classification of breast cancer potential like expert systems.