Alcoholism Detection via Wavelet Energy and Logistic Regression

DOI

10.2991/icitme-18.2018.33How to use a DOI?

Keywords

alcoholism; wavelet energy; logistic regression; detection; identification

Abstract

In this study, we proposed an application of alcoholism detection via wavelet energy and logistic regression. We collected data sets of 70 volunteers who signed up through advertising, among which 35 were with alcoholism and the rest were healthy. We first used wavelet energy (WN) to extract brain images features. Then, we employed logistic regression (LR) as the classification tool. Finally, we used 5-fold stratified cross validation to verify classifier performance. Our method achieves a sensitivity of 84.00± 3.86%, a specificity of 84.86± 3.03%, and an accuracy of 84.43± 1.42%. Our method gives better performance than HWT and ANN-GA.

Copyright

© 2018, 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  - Yuanyuan Tao
AU  - Felix Macdonald
PY  - 2018/08
DA  - 2018/08
TI  - Alcoholism Detection via Wavelet Energy and Logistic Regression
BT  - Proceedings of the 2018 International Conference on Information Technology and Management Engineering (ICITME 2018)
PB  - Atlantis Press
SP  - 164
EP  - 168
SN  - 1951-6851
UR  - https://doi.org/10.2991/icitme-18.2018.33
DO  - 10.2991/icitme-18.2018.33
ID  - Tao2018/08
ER  -