ArticleFrontiers in neuroscience2020
Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of the effects of proton pump inhibitors on the human gut microbiota.BMC microbiology · 2023Pooled it
- Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.Frontiers in psychology · 2026Review
- Integrating eye-tracking into psychometric research: computational approaches to explore response burden in questionnaire completion.Frontiers in psychology · 2026Article
- Eye movements powered by artificial intelligence identify asymptomatic carriers of familial Alzheimer's disease.Brain communications · 2025Article
- Age- and Sex-Based Developmental Biomarkers in Eye Movements.Brain sciences · 2024Article
- Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.Quantitative imaging in medicine and surgery · 2024Article
- Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination.Sensors (Basel, Switzerland) · 2023Article
- Machine-Learning-Based Disease Diagnosis: A Comprehensive Review.Healthcare (Basel, Switzerland) · 2022Review
- Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).Biochemical Society transactions · 2022Article
- Data on the Impact of Epidemic on Nursing Staff's Mental Health in the Context of Wireless Network.Journal of healthcare engineering · 2022Article
- Automated and accurate assessment for postural abnormalities in patients with Parkinson's disease based on Kinect and machine learning.Journal of neuroengineering and rehabilitation · 2021Article
- Predicting Visuo-Motor Diseases From Eye Tracking Data.Pacific Symposium on Biocomputing. Pacific Symposium on BiocomputingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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