%0 Journal Article
%T Evaluation of Logistic Regression and Neural Network Model With Sensitivity Analysis on Medical Datasets
%A Raghavendra B.K. & S.K. Srivatsa
%J International Journal of Computer Science and Security
%D 2011
%I Computer Science Journals
%X Logistic Regression (LR) is a well known classification method in the field of statistical learning. Itallows probabilistic classification and shows promising results on several benchmark problems.Logistic regression enables us to investigate the relationship between a categorical outcome anda set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universalnon-linear inference models and have gained extensive popularity in recent years. Researchactivities are considerable and literature is growing. The goal of this research work is to comparethe performance of logistic regression and neural network models on publicly available medicaldatasets. The evaluation process of the model is as follows. The logistic regression and neuralnetwork methods with sensitivity analysis have been evaluated for the effectiveness of theclassification. The classification accuracy is used to measure the performance of both themodels. From the experimental results it is confirmed that the neural network model withsensitivity analysis model gives more efficient result.
%K Artificial Neural Network
%K Classification Accuracy
%K Logistic Regression
%K Medical Dataset
%K Sensitivity Analysis.
%U http://cscjournals.org/csc/manuscript/Journals/IJCSS/volume5/Issue5/IJCSS-568.pdf