Alcoholism detection by medical robots based on Hu moment invariants and predator–prey adaptive-inertia chaotic particle swarm optimization. (October 2017)

• Record Type: Journal Article
• Authors: Zhang, Yu-Dong; Zhang, Yin; Lv, Yi-Ding; Hou, Xiao-Xia; Liu, Fang-Yuan; Jia, Wen-Juan; Yang, Meng-Meng; Phillips, Preetha; Wang, Shui-Hua
• Is Part Of: Computers & electrical engineering. Volume 63(2017)
• Page Start: 126
• Page End: 138
• Publication Date: 2017-10
• Subjects: Alcoholism -- Alcohol use disorder -- Predator–prey model -- Adaptive inertia -- Chaos theory -- Particle swarm optimization -- Genetic algorithm -- Firefly algorithm -- Single-hidden layer neural-network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.04.009
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: This study developed the key algorithms in an alcohol use disorder (AUD) detection robot. We used Hu moment invariants to extract features from brain slices. We proposed a novel predator–prey adaptive chaotic particle swarm optimization to train the classifier. 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.