According to literature, myopia has become the second most common eye disease in China, and the incidence of myopia is increasing year by year, and showing a trend of younger age. Previous researches have shown that the occurrence of myopia is mainly determined by poor eye habits, including reading and writing posture, eye length, and so on, and parents’ heredity. In order to better prevent myopia in adolescents, this paper studies the influence of related factors on myopia incidence in adolescents based on machine learning method. A feature selection method based on both univariate correlation analysis and multivariate correlation analysis is used to better construct a feature sub-set for model training. A method based on GBRT is provided to help fill in missing items in the original data. The prediction model is built based on SVM model. Data transformation has been used to improve the prediction accuracy. Results show that our method could achieve reasonable performance and accuracy.

Large-scale artificial neural networks have many redundant structures, making the network fall into the issue of local optimization and extended training time. Moreover, existing neural network topology optimization algorithms have the disadvantage of many calculations and complex network structure modeling. We propose a Dynamic Node-based neural network Structure optimization algorithm (DNS) to handle these issues. DNS consists of two steps: the generation step and the pruning step. In the generation step, the network generates hidden layers layer by layer until accuracy reaches the threshold. Then, the network uses a pruning algorithm based on Hebb’s rule or Pearson’s correlation for adaptation in the pruning step. In addition,, we combine genetic algorithm to optimize DNS(GA-DNS). Experimental results show that compared with traditional neural network topology optimization algorithms, GA-DNS can generate neural networks with higher construction efficiency, lower structure complexity, and higher classification accuracy.