Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
Data mining with decision trees and decision rules
Future Generation Computer Systems - Special double issue on data mining
Modeling the relationship between corporate strategy and wealth creation using neural networks
Computers and Operations Research - Neural networks in business
International Journal of Intelligent Systems in Accounting and Finance Management
Expert Systems with Applications: An International Journal
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
A hybrid approach of DEA, rough set and support vector machines for business failure prediction
Expert Systems with Applications: An International Journal
Prediction of influenza vaccination outcome by neural networks and logistic regression
Journal of Biomedical Informatics
Hi-index | 12.05 |
A nonlinear approach for detecting relative lymphopenia is suggested by using a health data record based on simple clinical parameters. Two classification methods, neural networks and decision trees, were applied to detect whether a patient has a positive or a negative lymphopenia outcome. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. All tested models were validated on the same out-of-sample dataset, and a 10-fold cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to their classification accuracy in the sense of the average hit rate, specificity and sensitivity. The results show that (1) the best neural network model slightly outperforms the decision tree model, (2) the reduced model provides even higher accuracy than the models with all available data, and (3) both methods similarly rank five important predictors of lymphopenia. The paper discusses the relevance of extracted features, and suggests some guidelines for further research.