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

394
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:
394
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.7K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.7K
Cancer Survival Analysis01:21

Cancer Survival Analysis

557
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
557
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
Prediction Intervals01:03

Prediction Intervals

2.9K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.9K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

188
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
188

You might also read

Related Articles

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

Sort by
Same author

Correction: Characterization and genome-informatic analysis of a novel lytic Pseudomonas mendocina phage vB_PmeS_STP12 suitable for phage therapy or biocontrol.

Molecular biology reports·2024
Same author

Characterization and genome-informatic analysis of a novel lytic mendocina phage vB_PmeS_STP12 suitable for phage therapy pseudomonas or biocontrol.

Molecular biology reports·2024
Same author

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana.

Health and technology·2022
Same author

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19).

IEEE access : practical innovations, open solutions·2022
Same author

Machine Learning Predictive Models for Coronary Artery Disease.

SN computer science·2021
Same author

Fuzzy based expert system for diagnosis of coronary artery disease in nigeria.

Health and technology·2021
Same journal

Toward Cybersecurity Testing and Monitoring of IoT Ecosystems.

SN computer science·2026
Same journal

Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.

SN computer science·2026
Same journal

Detecting Adverse Drug Events in Social Media: A Brief Literature Review.

SN computer science·2026
Same journal

TRAM: The Telecommunications-Related AcciMap Method.

SN computer science·2026
Same journal

A Combinatorial Approach to Synthetic Data Generation for Machine Learning.

SN computer science·2026
Same journal

To Signal or Not to Signal? A Non-cooperative Game-Theoretic Approach to Discretionary Communication Between Road Users.

SN computer science·2025
See all related articles

Related Experiment Video

Updated: Dec 5, 2025

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

1.8K

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery.

L J Muhammad1, Md Milon Islam2, Sani Sharif Usman3

  • 1Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria.

SN Computer Science
|October 16, 2020
PubMed
Summary
This summary is machine-generated.

Data mining models accurately predict COVID-19 recovery times and patient risk factors. The decision tree algorithm achieved 99.85% accuracy, offering a valuable tool for managing the pandemic.

Keywords:
COVID-19CoronavirusData miningDecision treePandemicPatients’ recovery

More Related Videos

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.6K
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

286

Related Experiment Videos

Last Updated: Dec 5, 2025

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

1.8K
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.6K
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

286

Area of Science:

  • Artificial Intelligence in Medicine
  • Data Mining and Machine Learning
  • Epidemiology and Public Health

Background:

  • The COVID-19 pandemic necessitates innovative solutions beyond clinical treatments.
  • Artificial intelligence (AI) and data mining offer potential for managing healthcare burdens and improving patient outcomes.
  • Predictive modeling can aid in diagnosing and prognosing COVID-19 (2019-nCoV) patients.

Purpose of the Study:

  • To develop and evaluate data mining models for predicting COVID-19 patient recovery.
  • To identify patient demographics and risk factors associated with COVID-19 recovery.
  • To compare the efficacy of various machine learning algorithms in predicting COVID-19 outcomes.

Main Methods:

  • Epidemiological data from COVID-19 patients in South Korea was utilized.
  • Several machine learning algorithms were applied: decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.
  • Models were developed using Python programming language to predict recovery duration and risk.

Main Results:

  • The decision tree model demonstrated superior performance with an overall accuracy of 99.85%.
  • Models predicted recovery timelines, high-risk age groups, and likelihood of quick recovery.
  • The decision tree algorithm outperformed other tested algorithms in predicting patient recovery.

Conclusions:

  • Data mining, particularly the decision tree algorithm, is highly effective for predicting COVID-19 recovery.
  • AI-driven predictive models can significantly support healthcare systems in managing the pandemic.
  • Accurate prediction of patient recovery aids in resource allocation and patient management strategies.