RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm - Wang, Shui-Hua; Jiang, Xianwei; Zhang, Yu-Dong (Frontiers in Neuroscience, 2021)
(Aim) Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading a wide range of potential symptoms. This method aims to propose a novel MS recognize method. (Methods) First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features (BWFs) are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA. (Results) Our method achieves a sensitivity of 98.00±0.95%, a specificity of 97.78±0.95%, and an accuracy of 97.89±0.94%. The area under the curve of our method is 0.9876. (Conclusion) The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods and two deep learning-based methods.