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

Cancer Survival Analysis

Cancer Survival Analysis

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...

You might also read

Related Articles

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

Sort by

Same author

Shared genetic architecture and pathways linking major depressive disorder and autoimmune thyroid disease: insights from common and rare variants.

Translational psychiatry·2026

Same author

[Association of Obesity Phenotypes With Cognitive Impairment and Genetic Stratification Analysis in Older Chinese Adults].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2025

Same author

GPR15 differentially regulates the effects of cigarette smoke exposure on Crohn's disease and ulcerative colitis.

Signal transduction and targeted therapy·2025

Same author

Comorbidity patterns associated with severe COVID-19 outcomes: A cohort study based on the UK Biobank.

PloS one·2025

Same author

Disease clusters and their genetic determinants following a diagnosis of depression: analyses based on a novel three-dimensional disease network approach.

Molecular psychiatry·2025

Same author

Genetic Determinants of the Association between Osteoarthritis and Psychiatric Disorders.

Depression and anxiety·2025

Search research articles

Related Experiment Video

Updated: Dec 19, 2025

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

Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development.

Can Hou¹, Xiaorong Zhong^2,3, Ping He^2,3

¹Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.

JMIR Medical Informatics

|June 9, 2020

Summary

This summary is machine-generated.

Machine learning models, particularly XGBoost, show promise for accurate breast cancer risk prediction in Chinese women. These models can aid in identifying high-risk individuals for targeted screening interventions.

Keywords:

XGBoost breast cancer deep neural network machine learning random forest

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

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

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

Related Experiment Videos

Last Updated: Dec 19, 2025

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

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

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

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

Area of Science:

Oncology
Biostatistics
Machine Learning

Background:

Accurate breast cancer prediction models are crucial for effective risk-based screening in China.
Current models need enhancement to meet the specific needs of the Chinese female population.

Purpose of the Study:

To evaluate and compare four machine learning algorithms for breast cancer prediction in Chinese women.
To assess model performance using 10 key breast cancer risk factors.

Main Methods:

Utilized a dataset of 7127 breast cancer cases and 7127 controls.
Employed repeated 5-fold cross-validation for robust performance evaluation.
Measured model efficacy using Area Under the Curve (AUC), sensitivity, specificity, and accuracy.

Main Results:

XGBoost, Random Forest, and Deep Neural Network significantly outperformed logistic regression in predicting breast cancer.
XGBoost achieved the highest AUC (0.742), followed by Deep Neural Network and Random Forest (both AUC 0.728).
Key predictive variables included main residence, number of live births, menopause status, age, and age at first birth.

Conclusions:

Novel machine learning algorithms, especially XGBoost, are effective for developing breast cancer prediction models.
These models can significantly aid in identifying high-risk women for targeted interventions in developing countries.