A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data

Author

Tunç, Taner

Source

Mathematical Problems in Engineering

Issue

Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2012-12-05

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Civil Engineering

Abstract EN

Logistic regression (LR) is a conventional statistical technique used for data classification problem.

Logistic regression is a model-based method, and it uses nonlinear model structure.

Another technique used for classification is feedforward artificial neural networks.

Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function.

In this study, a hybrid approach of model-based logistic regression technique and data-based artificial neural network was proposed for classification purposes.

The proposed approach was applied to lung cancer data, and obtained results were compared.

It was seen that the proposed hybrid approach was superior to logistic regression and feedforward artificial neural networks with respect to many criteria.

American Psychological Association (APA)

Tunç, Taner. 2012. A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-1001428

Modern Language Association (MLA)

Tunç, Taner. A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data. Mathematical Problems in Engineering No. 2012 (2012), pp.1-10.
https://search.emarefa.net/detail/BIM-1001428

American Medical Association (AMA)

Tunç, Taner. A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-1001428

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1001428