Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Chimeric Airway Model Enables Evaluation of Essential Genes In Vivo.

American journal of respiratory cell and molecular biology·2026

Same author

Biomaterial Selection Regulates Tissue Remodeling and Collagen Deposition in Composite Tracheal Grafts.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026

Same author

Protective effects of skin-derived precursor cell exosomes against UVB-induced skin photodamage.

Scientific reports·2026

Same author

Surgery-Induced Stress Promotes SOX9 Nuclear Localization and Increases COL2A1 in Tracheal Cartilage.

The Annals of otology, rhinology, and laryngology·2026

Same author

Endothelial Hydrogels Improve Microvascular Regeneration and Perfusion in Tracheal Scaffolds.

The Laryngoscope·2026

Same author

Pathologic wall shear stress in the trachea decreases epithelial differentiation and increases fibrosis.

American journal of respiratory cell and molecular biology·2026

Search research articles

Related Experiment Video

Updated: Dec 10, 2025

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

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

Yuxing Mao¹, Yinghong He¹, Lumei Liu¹

¹State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, China.

Frontiers in Neuroscience

|August 28, 2020

Summary

This study introduces a novel artificial intelligence (AI) method using random forests (RF) to classify neurological diseases based on eye movement patterns. The AI approach demonstrates improved accuracy in diagnosing conditions like Parkinson's and Alzheimer's disease.

Keywords:

decision tree disease discrimination eye movement long short-term memory random forest

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Dec 10, 2025

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Neurology
Artificial Intelligence
Biomedical Engineering

Background:

Eye movement disorders are linked to various neurological diseases, including Parkinson's disease and Alzheimer's disease (AD).
Establishing direct correlations between specific eye movement characteristics and disease pathology remains challenging due to incomplete understanding of disease mechanisms.

Purpose of the Study:

To develop and evaluate a robust disease classification method utilizing eye movement data.
To leverage artificial intelligence (AI) for improved pathological analysis of neurological conditions.

Main Methods:

Extraction of original features (pupil position, area) from eye movement images.
Utilizing long short-term memory (LSTM) networks to generate evolutionary features from original data.
Construction of a random forest (RF) classifier using decision trees (C4.5 rules) for disease classification through voting.

Main Results:

The proposed RF method exhibited significant robustness.
Achieved superior classification accuracy compared to previous methods.
Demonstrated the efficacy of AI in analyzing eye movement for disease diagnosis.

Conclusions:

The RF-based approach offers a promising tool for neurological disease classification using eye movement biomarkers.
AI technology presents significant advantages and potential for pathological analysis in ophthalmology and neurology.