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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

401
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:
401
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

6.2K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
6.2K
Classification of Illness01:17

Classification of Illness

8.4K
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...
8.4K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.0K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.0K

You might also read

Related Articles

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

Sort by
Same author

Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study.

Journal of medical Internet research·2025
Same author

HAPI: An efficient Hybrid Feature Engineering-based Approach for Propaganda Identification in social media.

PloS one·2024
Same author

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network.

Computational intelligence and neuroscience·2022
Same author

Identifying propaganda from online social networks during COVID-19 using machine learning techniques.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2020
Same journal

Harnessing deep learning for SNP-based disease prediction in genomics.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2025
Same journal

On utilizing modified TOPSIS with <i>R</i>-norm <i>q</i>-rung picture fuzzy information measure green supplier selection.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2023
Same journal

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2023
Same journal

A parametric analysis of AVA to optimise Netflix performance.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2023
Same journal

Convolutional neural network based children recognition system using contactless fingerprints.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2023
Same journal

Adoption and sustainability of bitcoin and the blockchain technology in Nigeria.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2023
See all related articles

Related Experiment Video

Updated: Dec 11, 2025

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

304

Machine learning based approaches for detecting COVID-19 using clinical text data.

Akib Mohi Ud Din Khanday1, Syed Tanzeel Rabani1, Qamar Rayees Khan1

  • 1Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, 185234 Jammu and Kashmir India.

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management
|August 25, 2020
PubMed
Summary
This summary is machine-generated.

This study used artificial intelligence (AI) and machine learning algorithms to classify COVID-19 clinical reports. Logistic regression and Multinomial Naïve Bayes achieved 96.2% accuracy in detecting the disease.

Keywords:
Artificial intelligenceCOVID-19EnsembleImperativeMachine learning

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.3K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

752

Related Experiment Videos

Last Updated: Dec 11, 2025

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

304
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.3K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

752

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Rapid technological advancements impact all sectors, including healthcare.
  • Artificial intelligence (AI) demonstrates significant potential in healthcare decision-making through data analysis.
  • The global spread of COVID-19 necessitates rapid diagnostic tools.

Purpose of the Study:

  • To develop a control system for detecting coronavirus using AI tools.
  • To classify textual clinical reports related to COVID-19.

Main Methods:

  • Utilized classical and ensemble machine learning algorithms.
  • Applied feature engineering techniques including Term Frequency/Inverse Document Frequency (TF/IDF) and Bag of Words (BOW).
  • Classified textual clinical reports into four distinct categories.

Main Results:

  • Logistic regression and Multinomial Naïve Bayes algorithms demonstrated superior performance.
  • Achieved a testing accuracy of 96.2% with these selected algorithms.
  • TF/IDF and BOW were effective feature engineering methods for this classification task.

Conclusions:

  • AI and machine learning are effective tools for diagnosing diseases like COVID-19 from clinical reports.
  • Classical machine learning models, specifically Logistic Regression and Multinomial Naïve Bayes, show high accuracy.
  • Recurrent neural networks are suggested for future research to potentially improve accuracy further.