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

Classification of Illness

Classification of Illness

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...

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

You might also read

Related Articles

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

Sort by

Same author

Predicting Factors Affecting the Reporting of Medical Errors by Nurses Using the Theory of Planned Behavior: A Study in Iran.

Health services insights·2026

Same author

Economic burden of knee replacement, a case of Bushehr, Iran in 2023-2024.

Cost effectiveness and resource allocation : C/E·2026

Same author

Technophobia and computer anxiety in older adults: a systematic review of the influencing factors.

BMC public health·2026

Same author

The long-term impact of the COVID-19 pandemic on mental and psychiatric service utilization in Iran: a 7-year longitudinal study from 2017 to 2024.

BMC psychiatry·2026

Same author

Modeling Factors Affecting Physicians' Acceptance of E-Prescriptions Using Structural Equation Modeling-Partial Least Squares Analysis: A Cross-Sectional Study.

Health science reports·2026

Same author

Determinants of the Cost of Illness in Iranian Prostate Cancer Patients.

Prostate cancer·2026

Search research articles

Related Experiment Video

Updated: Aug 19, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Supervised Machine Learning Approach to COVID-19 Detection Based on Clinical Data.

Azita Yazdani¹, Maryam Zahmatkeshan^2,3, Ramin Ravangard⁴

¹Department of Health Information Management, Clinical Education Research Center, Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.

Medical Journal of the Islamic Republic of Iran

|November 30, 2022

Summary

This summary is machine-generated.

Machine learning (ML) algorithms show promise for early COVID-19 detection. Support Vector Machine and C4.5 achieved high accuracy, with patient contact history being a key diagnostic factor.

Keywords:

Artificial Intelligence COVID-19 Classification Data mining Machine Learning

More Related Videos

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

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

Published on: October 10, 2018

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Aug 19, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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

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

Published on: October 10, 2018

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Epidemiology

Background:

The COVID-19 pandemic necessitated rapid, non-clinical solutions for diagnosis and control.
Artificial intelligence (AI) techniques offer potential for early detection and reducing healthcare system burden.

Purpose of the Study:

To evaluate the efficacy of Machine Learning (ML) algorithms for the early detection of COVID-19.
To compare the performance of six different ML algorithms in diagnosing COVID-19.

Main Methods:

Retrospective study utilizing a dataset of 10055 COVID-19 cases with 63 features (March-October 2020).
Comparison of C4.5, Support Vector Machine (SVM), Naive Bayes, Logistic Regression (LR), Random Forest, and K-Nearest Neighbor algorithms using Rapid Miner.
Performance evaluation based on precision, recall, accuracy, and f-measure.

Main Results:

Support Vector Machine (SVM) achieved 93.41% accuracy, and C4.5 achieved 91.87% accuracy.
The C4.5 decision tree identified "contact with a person who has COVID-19" as the most significant diagnostic criterion (Gini index).

Conclusions:

Machine learning approaches demonstrate a reasonable level of accuracy for COVID-19 diagnosis.
ML algorithms can serve as valuable tools for early COVID-19 detection and epidemic management.