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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

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

Sort by

Same author

Analyzing factors associated with sarcopenia in community-dwelling older adults.

Medicine·2026

Same author

Optimizing Maritime Search and Rescue Planning via Genetic Algorithms: Incorporating Civilian Vessel Collaboration.

Biomimetics (Basel, Switzerland)·2025

Same author

Effects of aquarobics on back pain, sleep, and memory in older women with chronic pain.

Medicine·2025

Same author

Effect of treadmill walking on cardiometabolic risk factors and liver function markers in older adults with MASLD: a randomized controlled trial.

BMC sports science, medicine & rehabilitation·2025

Same author

Advanced Glycation End Products and Mobility Decline: A Novel Perspective on Aging.

Healthcare (Basel, Switzerland)·2025

Same author

Relationship Between Lifestyle and Physical Fitness Among Older Women with Sarcopenia.

International journal of molecular sciences·2025

Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026

Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026

Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026

Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 29, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms.

Jaehyeong Lee¹, Yourim Yoon², Jiyoun Kim³

¹Department of IT Convergence, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.

Biomimetics (Basel, Switzerland)

|March 27, 2024

Summary

This summary is machine-generated.

Metaheuristic feature selection, using harmony search (HS) and genetic algorithms (GA), significantly improves machine learning for sarcopenia diagnosis. HS with support vector machines achieved the best diagnostic performance.

Keywords:

feature selection genetic algorithms harmony search machine learning metaheuristic sarcopenia

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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

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

Published on: March 21, 2021

Related Experiment Videos

Last Updated: Jun 29, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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

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

Published on: March 21, 2021

Area of Science:

Gerontology
Biomedical Informatics
Machine Learning

Background:

Sarcopenia diagnosis relies on accurate feature identification for machine learning models.
Effective feature selection is crucial for enhancing diagnostic efficacy in aging research.

Purpose of the Study:

To evaluate metaheuristic-based feature selection methods for improving machine learning-based sarcopenia diagnosis.
To compare harmony search (HS) and genetic algorithm (GA) for identifying key diagnostic features.

Main Methods:

Utilized data from the 8th Korean Longitudinal Study on Aging (KLoSA).
Applied HS and GA for feature selection.
Evaluated feature sets using decision tree, random forest, support vector machine, and naïve bayes algorithms.

Main Results:

The HS-derived feature set, when trained with a support vector machine, achieved an accuracy of 0.785 and a weighted F1 score of 0.782.
Metaheuristic-based feature selection outperformed traditional methods in this context.

Conclusions:

Metaheuristic-based feature selection offers a competitive advantage for sarcopenia diagnosis.
Further research into metaheuristics is recommended to advance sarcopenia diagnostic tools.