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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

147
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
147
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

153
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
153
Classification of Illness01:17

Classification of Illness

7.9K
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...
7.9K
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.2K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.2K
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

1.7K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
1.7K

You might also read

Related Articles

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

Sort by
Same author

An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble.

Sensors (Basel, Switzerland)·2023
Same author

Color Image Retrieval Method Using Low Dimensional Salient Visual Feature Descriptors for IoT Applications.

Computational intelligence and neuroscience·2023
Same author

An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images.

Sensors (Basel, Switzerland)·2022
Same author

On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment.

Computational intelligence and neuroscience·2022
Same author

Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction.

Computational intelligence and neuroscience·2022
Same author

Blind and Secured Adaptive Digital Image Watermarking Approach for High Imperceptibility and Robustness.

Entropy (Basel, Switzerland)·2021
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
See all related articles

Related Experiment Video

Updated: Sep 7, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk

Sushruta Mishra1, Hiren Kumar Thakkar2, Priyanka Singh3

  • 1School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar 751024, India.

Computational Intelligence and Neuroscience
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

A new memory-based metaheuristic attribute selection (MMAS) model improves chronic disease risk prediction accuracy to 94.5%. This method efficiently filters patient data, aiding in early diagnosis and decision support systems.

More Related Videos

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
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Related Experiment Videos

Last Updated: Sep 7, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
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
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Data Science

Background:

  • Predictive analytics are crucial for assessing chronic disorder risks.
  • Traditional attribute selection methods face challenges like complexity and high computational costs.
  • Heuristic optimization offers a more efficient approach by reducing computational load and removing irrelevant attributes.

Purpose of the Study:

  • To introduce a novel memory-based metaheuristic attribute selection (MMAS) model for optimizing chronic disorder data.
  • To enhance the accuracy and efficiency of chronic disease risk prediction.
  • To develop a cost-effective decision support system for medical experts.

Main Methods:

  • A buffer-enabled heuristic memory-based metaheuristic attribute selection (MMAS) model was developed for local neighborhood search.
  • Unsupervised K-means clustering was employed to filter outlier attributes from the data.
  • The Naive Bayes classifier was used for the final prediction of chronic disease risks.
  • Datasets included heart disease, breast cancer, diabetes, and hepatitis.

Main Results:

  • The MMAS model achieved a mean accuracy of 94.5% in predicting chronic disease risks.
  • Accuracy decreased to 93.5% when clustering was omitted, highlighting its importance.
  • High average precision (96.05%), recall (94.07%), and F-score (95.06%) were recorded.
  • The model demonstrated a low latency of 0.8 seconds.

Conclusions:

  • Heuristic-based attribute selection combined with clustering and classification significantly improves chronic disease diagnosis.
  • The proposed MMAS model offers a more accurate and computationally efficient approach to risk assessment.
  • This method can facilitate the development of effective decision support systems for medical professionals.