Alcoholism detection by medical robots based on Hu moment invariants and predator–prey adaptive-inertia chaotic particle swarm optimization

Yu Dong Zhang, Yin Zhang, Yi Ding Lv, Xiao Xia Hou, Fang Yuan Liu, Wen Juan Jia, Meng Meng Yang, Preetha Phillips, Shui Hua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)

Abstract

This work is aimed to develop the key algorithms used in medical robots, which can detect alcohol use disorder from structural magnetic resonance imaging of brains. We enrolled 30 alcoholic participants and 30 nonalcoholic participants. In the algorithm stage, we suggested to use Hu moment invariant to extract global features, and use single-hidden layer neural-network as the classifier. Afterwards, we proposed a novel predator–prey adaptive-inertia chaotic particle swarm optimization algorithm to train the classifier. The ten-fold stratified cross validation showed that our method achieves a sensitivity of 90.67 ± 3.16%, a specificity of 91.33 ± 3.06%, and an accuracy of 91.00 ± 1.41%. Our results are better than genetic algorithm, firefly algorithm, and particle swarm optimization. This proposes algorithm is effective in alcoholism detection. It can be installed on medical robots.

Original languageEnglish
Pages (from-to)126-138
Number of pages13
JournalComputers and Electrical Engineering
Volume63
DOIs
Publication statusPublished - Oct 2017
Externally publishedYes

Keywords

  • Adaptive inertia
  • Alcohol use disorder
  • Alcoholism
  • Chaos theory
  • Firefly algorithm
  • Genetic algorithm
  • Particle swarm optimization
  • Predator–prey model
  • Single-hidden layer neural-network

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