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

Cancer Survival Analysis01:21

Cancer Survival Analysis

633
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...
633
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

885
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
885

You might also read

Related Articles

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

Sort by
Same author

A dataset of breast cancer risk factors in Cuban women: Epidemiological evidence from Havana.

Data in brief·2024
Same author

Breast cancer risk estimation with intelligent algorithms and risk factors for Cuban women.

Computers in biology and medicine·2024
Same author

Epidemiological overview of snakebites in the Baja California peninsula, Mexico (2003-2018).

Gaceta medica de Mexico·2022
Same author

Model based on GA and DNN for prediction of mRNA-Smad7 expression regulated by miRNAs in breast cancer.

Theoretical biology & medical modelling·2018
Same author

An agent-based model of the fission yeast cell cycle.

Current genetics·2018
Same author

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

Sensors (Basel, Switzerland)·2012

Related Experiment Video

Updated: Jan 10, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.4K

Data preparation method for machine learning-based breast cancer risk prediction: A Cuban case study.

Jose Manuel Valencia-Moreno1, Everardo Gutierrez-Lopez1, Jose Angel Gonzalez-Fraga1

  • 1Universidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.

Methodsx
|November 24, 2025
PubMed
Summary

This study provides an open breast cancer risk factor dataset from Cuban women to develop predictive models. The data ensures integrity and supports machine learning for public health risk assessment.

Keywords:
Breast cancerCubaData preparationMachine learningPublic healthRisk factors

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

475
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

Related Experiment Videos

Last Updated: Jan 10, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.4K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

475
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

Area of Science:

  • Epidemiology
  • Public Health
  • Machine Learning

Background:

  • Breast cancer risk assessment is crucial for public health.
  • Developing accurate predictive models requires high-quality, accessible datasets.
  • Existing datasets may lack specific demographic or methodological rigor.

Purpose of the Study:

  • To present a curated dataset of breast cancer risk factors from Cuban women.
  • To facilitate the development and validation of predictive models for breast cancer risk.
  • To support machine learning applications in public health and epidemiology.

Main Methods:

  • Collected data from 1697 Cuban women between 2001 and 2018.
  • Implemented a reproducible methodology for data quality control and variable enrichment.
  • Ensured data integrity and compatibility with machine learning techniques.

Main Results:

  • An open dataset of breast cancer risk factors is now available.
  • The preprocessing methodology ensures data quality, traceability, and consistency.
  • Consistent prediction model performance was achieved across multiple metrics post-preprocessing.

Conclusions:

  • The dataset serves as a valuable tool for epidemiological studies and risk assessment.
  • The implemented methodology ensures the dataset's suitability for machine learning applications.
  • This resource can enhance public health strategies for breast cancer prevention and early detection.