Research on Visual Function Anomaly Classification Based on SMOTE + Boosting Multiple Sampling Algorithm

Abstract

Binocular vision refers to the process of both eyes simultaneously focusing on an object and accurately reflecting the external spatial environment. Binocular vision is constantly required in daily life. With the rapid development of artificial intelligence technology, its application in binocular vision can assist doctors in rapid diagnosis and treatment, which is of great significance for the early detection and treatment of strabismus and amblyopia. This paper primarily researched the problem of convergence and accommodation anomalies in binocular visual function. Firstly, the SMOTE algorithm was used to oversample the dataset and balance the sample of each label category. Then, the random forest algorithm was employed as the basic model for the three training processes in the boosting algorithm. Finally, the models generated from these three trainings were integrated, and the final result was obtained by voting. Comparing the performance differences of the models on the test set before and after the three sampling trainings indicated that the integrated model had a better classification effect on the dataset.