An introduction to variable and feature selection
The Journal of Machine Learning Research
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Constructing of the risk classification model of cervical cancer by artificial neural network
Expert Systems with Applications: An International Journal
Prediction of vehicle reliability performance using artificial neural networks
Expert Systems with Applications: An International Journal
A review of feature selection techniques in bioinformatics
Bioinformatics
Computers & Mathematics with Applications
Classıfıcation of sleep apnea by using wavelet transform and artificial neural networks
Expert Systems with Applications: An International Journal
Prediction of diesel engine performance using biofuels with artificial neural network
Expert Systems with Applications: An International Journal
An expert system for perfume selection using artificial neural network
Expert Systems with Applications: An International Journal
Artificial neural network based modelling of the Marshall Stability of asphalt concrete
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A sequential neural network model for diabetes prediction
Artificial Intelligence in Medicine
Hi-index | 12.05 |
Classification is a frequently used decision making tool, however there are many classification methods and these seldom provide adequate and consistent results. In this paper we compare the classification efficiency of neural networks (NN) to more traditional methods such as LR (LR), in the context of identifying American Indian/Alaskan Native (AI/AN) elders who are at risk of developing diabetes. Feature selection is an important first step in building these classification models. We used both stepwise selection and genetic algorithm (GA) to identify features related to diabetes. Each LR and NN models were built twice, once based features identified by stepwise regression and second using features identified using genetic algorithm. Analysis of results from this approach lead to several conclusions: (a) although both LR and NN models exhibit similar classification ability, NN models were marginally better compared to LR models. (b) While the ROC value of these two models were the same (ROC=1), the type of feature selection methodology had no impact on the sensitivity and specificity of these models. In conclusion results from our study shows that although both these models are equally capable of identifying AI/AN elders at risk of developing diabetes, NN models are marginally better.