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...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

You might also read

Related Articles

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

Sort by

Same author

Explainable detection of goldenhar syndrome using an OD-Mamba backbone for rare craniofacial disorder diagnosis.

Scientific reports·2026

Same author

Domain-robust vision transformer with hierarchical swin encoding for explainable low-latency driver drowsiness detection.

Scientific reports·2026

Same author

Modular training resources for bioimage analysis.

Journal of microscopy·2026

Same author

Deciphering Mental Illness: Neurons vs. Neuroglia.

Journal of clinical psychopharmacology·2026

Same author

A Material Roadmap for Translational and Sustainable Point-of-Care Diagnostics Through Bacterial Cellulose: A Comprehensive Review.

ACS applied bio materials·2026

Same author

Liposomal Methylglyoxal Targets Virulence and Intracellular Persistence to Overcome Amphotericin B Resistance in <i>Cryptococcus neoformans</i>.

International journal of molecular sciences·2026

Search research articles

Related Experiment Video

Updated: Jan 1, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Comparing different supervised machine learning algorithms for disease prediction.

Shahadat Uddin¹, Arif Khan^2,3, Md Ekramul Hossain²

¹Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Room 524, SIT Building (J12), Darlington, NSW, 2008, Australia. shahadat.uddin@sydney.edu.au.

BMC Medical Informatics and Decision Making

|December 23, 2019

Summary

This summary is machine-generated.

Supervised machine learning algorithms like Random Forest show superior accuracy for disease prediction compared to Support Vector Machine and Naïve Bayes. This research aids in selecting optimal algorithms for health data analysis.

Keywords:

Disease prediction Machine learning Medical data Supervised machine learning algorithm

More Related Videos

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

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

Published on: July 22, 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

Related Experiment Videos

Last Updated: Jan 1, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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

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

Published on: July 22, 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

Area of Science:

Data Mining
Health Informatics
Computational Biology

Background:

Supervised machine learning (ML) is a key data mining technique.
ML shows promise for disease risk prediction using health data.
This study focuses on comparing ML algorithms for disease prediction.

Purpose of the Study:

Identify trends in supervised ML algorithm usage for disease prediction.
Compare the performance and application of various supervised ML algorithms.
Guide researchers in selecting appropriate ML methods for disease risk prediction.

Main Methods:

Conducted an extensive literature search on Scopus and PubMed.
Selected 48 articles comparing multiple supervised ML algorithms for single disease prediction.
Analyzed algorithm frequency and predictive accuracy.

Main Results:

Support Vector Machine (SVM) was most frequent (29 studies), followed by Naïve Bayes (23 studies).
Random Forest (RF) demonstrated superior comparative accuracy.
RF achieved the highest accuracy in 53% of studies where it was applied, versus 41% for SVM.

Conclusions:

Provides a comprehensive overview of supervised ML algorithm performance in disease prediction.
Highlights Random Forest as a highly accurate method.
Informs algorithm selection for future disease prediction research.