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| Evaluation of Logistic Regression and Neural Network Model With Sensitivity Analysis on Medical Datasets
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Full
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Complete Issue PDF(4.17MB) |
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Volume: 5 Issue: 5 |
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Pages: NULL |
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Publication
Date: November / December 2011 |
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ISSN
(Online): 1985-1553 |
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Pages |
503 - 511 |
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Author(s) |
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Published
Date |
15-12-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Artificial neural network, Classification accuracy, Logistic regression, Medical dataset, Sensitivity analysis |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. Google Scholar |
| 3. Scribd |
| 4. Bielefeld Academic Search Engine (BASE) |
| 5. Academic Journals Database |
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| Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result. |
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| B.K. Raghavendra : Colleagues
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| S.K. Srivatsa : Colleagues
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