Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

147
Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
147
Depression: Overview01:18

Depression: Overview

294
Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
294
Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

181
Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...
181

You might also read

Related Articles

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

Sort by
Same author

IL-4-mediated monocyte differentiation modulates CD163 expression and PRRSV infection.

Frontiers in microbiology·2026
Same author

Differential toll-like receptor signaling pathways shape IL-1β-dependent inflammatory responses in chicken macrophages.

Poultry science·2026
Same author

IL-27-mediated hematopoietic dysregulation exacerbates disease severity in severe fever with thrombocytopenia syndrome virus infection.

Science translational medicine·2026
Same author

Prognostic impact of <i>NF1</i> mutation in Korean cohort with glioblastoma.

Frontiers in neurology·2026
Same author

Plasma metabolomic signatures associated with inhaled corticosteroid requirements in clinically stable asthma: a prospective cross-sectional study.

Respiratory research·2026
Same author

Long-term cross-variant Fc-mediated immune responses against SARS-CoV-2 induced by a heterologous adenoviral/inactivated virus prime-boost vaccination strategy.

NPJ vaccines·2026

Related Experiment Video

Updated: Jul 29, 2025

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.9K

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.

Payam Hosseinzadeh Kasani1,2, Jung Eun Lee2, Chihyun Park2,3

  • 1Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea.

Frontiers in Nutrition
|May 25, 2023
PubMed
Summary

Machine learning models accurately identified depression using health data, highlighting the importance of nutritional and dietary factors. This approach offers a promising tool for early diagnosis and disease control.

Keywords:
classificationclinical depressiondepressioninterpretabilitymachine learningnutrition

More Related Videos

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Jul 29, 2025

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.9K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

7.6K

Area of Science:

  • Computational biology and bioinformatics
  • Public health and epidemiology
  • Nutritional science

Background:

  • Depression is a global health concern with significant implications for overall health.
  • Early diagnosis of depressive symptoms is crucial for effective management and prevention.
  • The role of nutritional and dietary factors in depression is increasingly recognized but complex to assess.

Purpose of the Study:

  • To evaluate the efficacy of machine learning (ML) decision support systems in identifying depression.
  • To analyze the association between nutritional/dietary factors and depression using ML.
  • To explore the interpretability of ML models for understanding depression risk factors.

Main Methods:

  • Utilized the Korean National Health and Nutrition Examination Survey dataset.
  • Employed uniform manifold approximation and projection (UMAP) and Pearson correlation for data exploration.
  • Trained and optimized various ML classifiers (e.g., XGBoost, Random Forest) using grid search and cross-validation.
  • Assessed model performance using metrics like accuracy, AUC, precision, recall, and F1 score.
  • Interpreted model predictions using ELI5, partial dependence plots, and SHAP values.

Main Results:

  • The best performing ML models achieved high accuracy (up to 86.18%) and AUC (up to 85.34%) in classifying depression.
  • Feature importance analysis identified key nutritional and dietary markers associated with depression risk.
  • Explainable AI techniques provided insights into both population-level and individual-level depression predictions.

Conclusions:

  • Supervised machine learning, particularly with fine-tuned models and large datasets, demonstrates high accuracy in classifying depressive disorders.
  • This ML-based approach offers a viable and effective strategy for depression screening and disease control.
  • Interpretable ML models can elucidate the complex interplay between nutrition and depression, aiding in risk identification.