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

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

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

An interpretable predictive model for depression risk in diabetic patients: A web-based application using NHANES

Yishi Li¹, Tong Ren¹, Guanghong Zhou²

¹Department of Neurointerventional Center, The Third People's Hospital Affiliated Dalian University of Technology, Dalian, Liaoning, China.

|June 27, 2026

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Durable Core-Shell Scatterer Coating with Heat Storage for Radiative Cooling.

ACS applied materials & interfaces·2026

Same author

GSDMD/Caspase-1-based evaluation of dydrogesterone in preventing recurrence after hysteroscopic endometrial polypectomy.

Clinics (Sao Paulo, Brazil)·2026

Same author

New aminopiperidine-azoles as antifungal agents against Candida albicans with mechanistic insights into the cAMP-PKA pathway.

Bioorganic & medicinal chemistry letters·2026

Same author

27-Hydroxycholesterol Induces Multidrug Resistance in Estrogen Receptor-Positive Breast Cancer Cells via Elevation of HER2.

Journal of applied toxicology : JAT·2026

Same author

Diversity-Oriented Synthesis of Diazabicyclo[3.2.1]octanes and Dihydroquinolines via Phosphine-Catalyzed Domino Reactions of <i>o</i>-Amino Arylaldimines with Allenoates.

Organic letters·2026

Same author

Green synthesis of biscoumarin derivatives as anti-psoriatic agents targeting PI3K/AKT/JAK2 signaling pathways.

Bioorganic chemistry·2026

This summary is machine-generated.

Machine learning accurately predicts depression risk in adults with diabetes. An interpretable Support Vector Machine model identifies key risk factors, aiding early intervention and personalized screening.

Area of Science:

Medical Informatics
Psychiatry
Public Health

Background:

Depression frequently co-occurs with diabetes, worsening health outcomes.
Identifying individuals with diabetes at high risk for depression is difficult due to complex risk factors.
Machine learning (ML) can improve depression risk prediction, but requires methods to handle imbalanced data and ensure interpretability for clinical use.

Purpose of the Study:

To develop and evaluate an interpretable, class-imbalance-aware ML model for predicting depression risk in adults with diabetes.
To utilize nationally representative data for robust model development and validation.
To ensure clinical applicability through interpretability and utility assessments.

Main Methods:

Analysis of cross-sectional data from 1140 US adults with diabetes (NHANES, 2007-2018).

Keywords:

NHANES depression diabetes machine learning predictive model

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

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

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

Published on: May 10, 2022

Related Experiment Videos

Last Updated: Jun 28, 2026

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

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

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

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

Published on: May 10, 2022

Depression defined by Patient Health Questionnaire-9 score ≥10; predictors included demographic, clinical, lifestyle, and socioeconomic factors.

Class imbalance addressed using sampling and cost-sensitive learning; seven ML models evaluated, with Support Vector Machine (SVM) selected for interpretability via SHapley Additive exPlanations (SHAP) and clinical utility via decision curve analysis.

Main Results:

The SVM model, optimized for class imbalance, showed superior performance in detecting depression.
Key predictors identified by SHAP included chest pain, poverty-income ratio, sleep duration, sex, body mass index, physical activity, triglyceride levels, and diet quality.
Decision curve analysis confirmed the model's clinical utility for screening, especially at lower risk thresholds.

Conclusions:

An interpretable, imbalance-aware SVM model effectively predicts depression risk in adults with diabetes.
The model supports individualized risk stratification, offering a tool for precision screening and early intervention.
Development of an interactive web application enhances accessibility for risk prediction and explanation.