Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Medical research shows that eye movement disorder is related to many kinds of neurological diseases. Eye movement characteristics can be used as biomarkers of Parkinson's disease, Alzheimer's disease, schizophrenia and other diseases. However, due to the unknown medical mechanism of some diseases, it is difficult to establish an intuitive correspondence between eye movement characteristics and diseases. In this paper, we propose a classification method of diseases based on decision tree and random forest. Firstly, a variety of experimental schemes are designed to obtain eye movement images, and information such as pupil position and area are extracted as original features. Secondly, with the original features as training samples, the LSTM network is used to build classifiers, and the classification results of the samples are regarded as the evolutionary features. After that, multiple decision trees are constructed according to C4.5 rules based on the evolutionary features. Finally, a random forest is constructed with these decision trees, and the results of disease classification are determined by voting. Experiments show that the classification accuracy of random forest is obviously better than the performance of the previous classifiers, and has good robustness. This study shows that the application of advanced AI technology in the pathological analysis of eye movement has obvious advantages and good prospects.