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

Classification of Illness01:17

Classification of Illness

8.3K
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...
8.3K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

350
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
350
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

959
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
959

You might also read

Related Articles

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

Sort by
Same author

Distribution of <i>CYP2C19</i> genetic polymorphisms and pharmacogenomic implications in 11,710 patients with cardiovascular and cerebrovascular conditions: a large population-based study in eastern China.

Frontiers in genetics·2026
Same author

Adverse childhood experiences and problematic gambling among young adults: a moderated serial mediation analysis.

Scientific reports·2026
Same author

Relationships between problematic social media use and social comparison: a meta-analysis.

BMC psychology·2026
Same author

Mechanistic Insights into Ginkgo Biloba Extract's Anti-Inflammatory Effects in COPD: Regulation of Th1/Th2 Balance via the p38 MAPK Pathway.

Current medicinal chemistry·2026
Same author

Wisdom and Life Purpose as Predictors of Mental Well-Being Among Middle-Aged to Older Adults: Cross-Sectional Study.

JMIR mental health·2026
Same author

Psychometric Evaluation of the Revised 16-Item Rushton Moral Resilience Scale Among Iranian Nurses.

Journal of nursing measurement·2026

Related Experiment Video

Updated: Nov 21, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms.

Ibrahim Arpaci1, Shigao Huang2, Mostafa Al-Emran3

  • 1Department of Computer Education and Instructional Technology, Tokat Gaziosmanpasa University, Tokat, Turkey.

Multimedia Tools and Applications
|January 13, 2021
PubMed
Summary

This study introduces a new diagnostic model for COVID-19 using clinical features, offering a faster and cheaper alternative to RT-PCR testing. The CR classifier achieved 84.21% accuracy, aiding early detection, especially where resources are limited.

Keywords:
COVID-19Classification algorithmsDiagnosisMachine learningNovel coronavirusPrediction

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

198
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.6K

Related Experiment Videos

Last Updated: Nov 21, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

198
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.6K

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Infectious Disease Diagnostics

Background:

  • Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the standard for COVID-19 confirmation but faces limitations like reagent shortages, time constraints, and specialized laboratory requirements.
  • Previous alternatives, including Chest CT and X-ray imaging with deep learning, present challenges such as radiation exposure, high costs, and limited device availability.
  • A need exists for rapid, cost-effective diagnostic tools for COVID-19, particularly in resource-limited settings.

Purpose of the Study:

  • To develop and evaluate predictive models for COVID-19 diagnosis using clinical features.
  • To identify the most accurate machine learning classifier for distinguishing between positive and negative COVID-19 cases.
  • To provide an accessible diagnostic alternative when RT-PCR testing is insufficient.

Main Methods:

  • Retrospective analysis of 114 COVID-19 cases from Taizhou Hospital, Zhejiang Province, China.
  • Development of six predictive models utilizing distinct classifiers: BayesNet, Logistic, IBk, CR, PART, and J48.
  • Models were trained and validated based on 14 selected clinical features.

Main Results:

  • The CR (Classification and Regression) meta-classifier demonstrated the highest accuracy at 84.21% in predicting COVID-19 status.
  • All developed models utilized 14 clinical features for prediction.
  • The study identified CR as the most effective classifier among the six evaluated.

Conclusions:

  • The developed CR-based predictive model offers a promising, accurate, and efficient method for COVID-19 diagnosis.
  • This approach can significantly aid in the early detection of COVID-19, especially in scenarios with limited RT-PCR availability.
  • The findings are particularly relevant for developing countries facing shortages of testing kits and specialized diagnostic facilities.