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

Abstract

Medical research shows that eye movement disorders are related to many kinds of neurological diseases. Eye movement characteristics can be used as biomarkers of Parkinson’s disease, Alzheimer’s disease (AD), 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 disease classification method based on decision tree and random forest (RF). First, a variety of experimental schemes are designed to obtain eye movement images, and information such as pupil position and area is extracted as original features. Second, with the original features as training samples, the long short-term memory (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 built according to the C4.5 rules based on the evolutionary features. Finally, a RF is constructed with these decision trees, and the results of disease classification are determined by voting. Experiments show that the RF method has good robustness and its classification accuracy is significantly better than the performance of previous classifiers. This study shows that the application of advanced artificial intelligence (AI) technology in the pathological analysis of eye movement has obvious advantages and good prospects.