Logistic Regression Analysis on the Survival Time for Patients with Lung Cancer

Sheng Ma

doi:10.4028/www.scientific.net/AMM.713-715.1757

Paper Titles

Improved Genetic Algorithm on Minimum Spanning Tree of Length Constraint Problem
p.1737

Improved RSA Algorithm and Application in Digital Signature
p.1741

Intelligent Optimization of Urban Real-Time Traffic Route Based on DNA Non-Dominated Sorting Genetic Algorithm
p.1746

Literature Review on Hidden Markov Model-Based Sequential Data Clustering
p.1750

Logistic Regression Analysis on the Survival Time for Patients with Lung Cancer
p.1757

Logistics Route Optimization Based on Improved Fish Swarm Algorithm
p.1761

MaxMining: A Novel Algorithm for Mining Maximal Frequent Itemset
p.1765

Method for Selecting Storage Enterprises Based on IFHA
p.1769

Modeling Method Based on Smooth Support Vector Regression with Independent Component Analysis Feature Extraction
p.1773

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715Logistic Regression Analysis on the Survival Time...

Logistic Regression Analysis on the Survival Time for Patients with Lung Cancer

Abstract:

In the paper, the survival time of patients with lung cancer is inferred based on binary classification variables Logistic regression linear model and the data analysis is implemented by R software.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

1757-1760

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.1757

Citation:

Cite this paper

Online since:

January 2015

Authors:

Sheng Ma*

Keywords:

Logistic Regression, Lung Cancer, R Software, Survival Time

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] JN and R. W. M. Wedderburn, Generalized Linear Models, J.R. Statist. Soc. A, 135 (1972), 370–384.

Google Scholar

[2] Cyrus. RM, Nitin RP. Exact logistic regression：theory and examples. Stat. in Med, 15(1995), 2143-2160.

Google Scholar

[3] Johnson D. Applied Multivariate methods for Data Analysts. Higher Education Press, (2005).

Google Scholar

[4] Ross, S.M. Simulation, Posts & Telecom Press, (2007).

Google Scholar

[5] http: /www. r-project. org.

Google Scholar