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

442
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:
442
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
Cancer Survival Analysis01:21

Cancer Survival Analysis

604
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...
604

You might also read

Related Articles

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

Sort by
Same author

Noradrenergic control of bone marrow and thymus by AgRP neurons is impaired in experimental multiple sclerosis.

Cell reports·2025
Same author

Relationship between Continuum of Hurst Exponents of Noise-like Time Series and the Cantor Set.

Entropy (Basel, Switzerland)·2021
Same author

Volatility estimation for COVID-19 daily rates using Kalman filtering technique.

Results in physics·2021
Same author

Mesenchymal stem cells instruct a beneficial phenotype in reactive astrocytes.

Glia·2020

Related Experiment Video

Updated: Dec 31, 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

8.6K

Supervised machine learning models applied to disease diagnosis and prognosis.

Maria C Mariani1, Osei K Tweneboah2, Md Al Masum Bhuiyan2

  • 1Department of Mathematical Sciences, University of Texas, El Paso, United States.

AIMS Public Health
|January 8, 2020
PubMed
Summary

This study compares five machine learning (ML) algorithms for diagnosing cancer and heart disease. Random Forest (RF) excelled in breast cancer prediction, while Principal Component Regression (PCR) was best for heart disease prognosis.

Keywords:
breast cancerheart diseasemachine learningpredictive modelssupervised learning

More Related Videos

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

2.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.3K

Related Experiment Videos

Last Updated: Dec 31, 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

8.6K
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

2.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.3K

Area of Science:

  • Computational biology and bioinformatics
  • Medical data analysis and machine learning

Background:

  • Accurate diagnosis and prognosis of cancer and heart disease are critical for patient outcomes.
  • Machine learning (ML) offers powerful tools for analyzing complex medical datasets.

Purpose of the Study:

  • To analyze and compare the diagnostic and prognostic capabilities of five ML algorithms on cancer and heart disease data.
  • To identify key variables influencing cancer and heart disease.
  • To evaluate the predictive accuracy of ML models using Receiver Operating Characteristic (ROC) curves.

Main Methods:

  • Utilized five distinct Machine Learning (ML) algorithms for data analysis.
  • Compared the predictive performance of each ML algorithm on breast cancer and heart disease datasets.
  • Employed Receiver Operating Characteristic (ROC) curves to compute prediction accuracy.

Main Results:

  • Identified significant variables contributing to cancer and heart disease.
  • Random Forest (RF) demonstrated superior performance in breast cancer data analysis.
  • Principal Component Regression (PCR) achieved the best performance for heart disease data analysis.

Conclusions:

  • Machine learning algorithms can effectively aid in the diagnosis and prognosis of cancer and heart disease.
  • Specific ML algorithms, such as RF for breast cancer and PCR for heart disease, show distinct advantages.
  • Variable importance analysis is crucial for targeted prediction and understanding disease drivers.