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

Changes in the Appendicular Skeleton with Age01:09

Changes in the Appendicular Skeleton with Age

3.2K
The upper and lower limb initially develops as a small bulge called a limb bud, which appears on the lateral side of the early embryo. The upper limb bud appears near the end of the fourth week of development, with the lower limb bud appearing shortly after.
Initially, the limb buds consist of a core of mesenchyme covered by a layer of ectoderm. The ectoderm at the end of the limb bud thickens to form a narrow crest called the apical ectodermal ridge. This ridge stimulates the underlying...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Use of artificial intelligence and health-related life satisfaction among older adults: A structural equation modeling study.

Digital health·2026
Same author

Health Belief Model Constructs Affect Influenza Vaccine Uptake in Kidney Transplant Recipients.

Western journal of nursing research·2022
See all related articles

Related Experiment Video

Updated: May 3, 2026

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.7K

A Machine Learning Model for Predicting Sarcopenia Among Middle-Aged Adults: Development and External Validation.

Hye Jin Chong1

  • 1Department of Nursing, Sunchon National University, Suncheon-si, Republic of Korea.

JMIR Medical Informatics
|August 27, 2025
PubMed
Summary

This study developed a machine learning model to predict sarcopenia risk in middle-aged adults. The model accurately identifies individuals at risk, enabling early intervention for better long-term health.

Keywords:
machine learningmiddle aged adultspredictive modelrisk factorssarcopenia

More Related Videos

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles
04:00

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles

Published on: July 26, 2024

661
The Creation of a Rat Model for Osteosarcopenia via Ovariectomy
03:52

The Creation of a Rat Model for Osteosarcopenia via Ovariectomy

Published on: February 21, 2025

498

Related Experiment Videos

Last Updated: May 3, 2026

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.7K
Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles
04:00

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles

Published on: July 26, 2024

661
The Creation of a Rat Model for Osteosarcopenia via Ovariectomy
03:52

The Creation of a Rat Model for Osteosarcopenia via Ovariectomy

Published on: February 21, 2025

498

Area of Science:

  • Gerontology and Public Health
  • Biomedical Informatics
  • Muscle Physiology

Background:

  • Sarcopenia, a common muscle disorder in older adults, necessitates early detection in middle-aged populations for improved health outcomes.
  • Early identification of sarcopenia can reduce future healthcare burdens and enhance the quality of life for aging individuals.
  • Machine learning (ML) offers potential for analyzing complex datasets to identify sarcopenia risk factors, addressing an unmet clinical need.

Purpose of the Study:

  • To develop and externally validate a machine learning (ML) model for predicting sarcopenia risk in middle-aged adults.
  • Utilize a nationally representative dataset to ensure the model's generalizability and applicability.
  • Establish a tool for early identification and intervention of sarcopenia in midlife populations.

Main Methods:

  • Analysis of data from 1926 middle-aged adults (40-64 years) from the 2022 Korea National Health and Nutrition Examination Survey (KNHANES).
  • Sarcopenia diagnosis based on 2019 Asian Working Group for Sarcopenia criteria (low muscle mass and reduced muscle strength).
  • Four ML algorithms (random forest, SVM, XGBoost, logistic regression) were employed, with the top model validated on an external cohort (2247 participants, 2023 KNHANES).

Main Results:

  • The logistic regression model exhibited the best performance with an AUC of 0.85, sensitivity of 0.92, and F2-score of 0.66.
  • External validation using the 2023 KNHANES dataset confirmed the model's robust predictive capabilities.
  • The developed ML model demonstrates strong potential for widespread application in identifying sarcopenia risk.

Conclusions:

  • An externally validated ML model accurately identifies sarcopenia risk in middle-aged adults.
  • Early detection and tailored interventions during midlife are crucial for mitigating sarcopenia and optimizing long-term health.
  • This study highlights the utility of ML in leveraging national health data for proactive public health strategies.