Prediction Using Logistic Regression Analysis of Peripheral Vascular Disease

DOI

10.2991/icemi-16.2016.48How to use a DOI?

Keywords

Logistic; peripheral vascular disease; regression model; clinical evidence

Abstract

Logisic regression model is to study the response variable is an important analytical method for non-continuous variables. Linear regression models and quantitative analysis is one of the most commonly used data mining methods of statistical analysis, linear regression analysis but generally require the response is a continuous variable, the data distribution is normal conditions. This study used logistic regression analysis to predict the study of peripheral vascular disease in the carotid atherosclerosis disease prediction model was established to provide scientific basis for the clinical treatment of peripheral vascular disease.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanan Li
AU  - Xiaona Guo
AU  - Chunsheng Yan
PY  - 2016/07
DA  - 2016/07
TI  - Prediction Using Logistic Regression Analysis of Peripheral Vascular Disease
BT  - Proceedings of the 2016 International Conference on Economics and Management Innovations
PB  - Atlantis Press
SP  - 237
EP  - 240
SN  - 2352-538X
UR  - https://doi.org/10.2991/icemi-16.2016.48
DO  - 10.2991/icemi-16.2016.48
ID  - Li2016/07
ER  -