关键词: 遗传算法, 特征选择, 帕金森病, 种群大小, 进化代数

Abstract: To assist doctors in better diagnosis of patients with Parkinson’s disease, genetic algorithm is used to optimize the gait characteristics and support vector machine parameters. In the feature selection section, the gait features and support vector machine parameters are generated in a binary coded manner and input into the genetic algorithm. Taking into account the search performance and runtime of the genetic algorithm, the two parameters of the population size and number of evolutionary generation in the algorithm are optimized to find the appropriate population size and number of evolutionary generation. The experimental results show that the average accuracy rate is 85.11%, compared with other feature selection algorithms, the accuracy rate is up to 14% higher. It shows that the use of this method can effectively remove the gait characteristics of patients with Parkinson’s disease, which is beneficial to the diagnosis of patients.

Key words: genetic algorithm, feature selection, Parkinson’s disease, population size, number of evolutionary generation