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Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22875–22896 | Cite as

Cat Swarm Optimization applied to alcohol use disorder identification

  • Yu-Dong Zhang
  • Yuxiu Sui
  • Junding Sun
  • Guihu Zhao
  • Pengjiang Qian
Article

Abstract

(Aim) Alcohol use disorder may put health at risk and cause serious health problems. It is of increasing importance to identify alcohol use disorder as early as possible. (Method) This study proposed a computer-vision based technique. The dataset was scanned by magnetic resonance imaging in China participating hospitals. Afterwards, we combined wavelet entropy, two-layer feedforward neural network, and cat swarm optimization (CSO). The CSO mimics the behavior of cat and is composed of two modes: seeking mode and tracing mode. (Results) The results showed that our method achieves a sensitivity of 91.84 ± 1.66%, a specificity of 92.40 ± 1.22%, and an accuracy of 92.13 ± 0.70%. Using grid searching approach, we found the classification performance is the best, when decomposition level is assigned with 2 and the mixture ratio is assigned with a value of 0.8. (Conclusion) The CSO is superior to four bioinspired algorithms: genetic algorithm, immune genetic algorithm, particle swarm optimization, and chaotic self-adaptive particle swarm optimization. In addition, our proposed alcoholism identification system is superior to four state-of-the-art alcoholism detection approaches.

Keywords

Alcohol user disorder Pattern recognition Cat swarm optimization Two-layer feedforward neural network Wavelet entropy Magnetic resonance imaging Cross validation 

Notes

Acknowledgments

This paper is financially supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Project of Science and Technology of Henan Province (172102210272), Program for Science & Technology Innovation Talents of Henan Province (174100510009), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601, HGAMTL-1703), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (17-259-05-011 K)., Henan Key Research and Development Project (182102310629), National key research and development plan (2017YFB1103202)

Compliance with ethical standards

Conflict of interest

The authors declare there is no conflict of interest with regard to this submission.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuoPeople’s Republic of China
  2. 2.Department of InformaticsUniversity of LeicesterLeicesterUK
  3. 3.Department of PsychiatryNanjing Medical UniversityNanjingChina
  4. 4.National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaPeople’s Republic of China
  5. 5.School of Digital MediaJiangnan UniversityWuxiChina

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