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

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...

You might also read

Related Articles

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

Sort by

Same author

Genome-scale prediction of gene ontology from mass fingerprints reveals new metabolic gene functions.

Life science alliance·2025

Same author

Extremely Elevated High-density Lipoprotein Cholesterol and the Risk of Atrial Fibrillation: The Suita Study.

Journal of epidemiology·2025

Same author

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.

JMIR cardio·2025

Same author

An inverse association of cerebral amyloid-β deposition and serum docosahexaenoic acid levels in cognitively normal older adults in Japan.

Journal of Alzheimer's disease : JAD·2025

Same author

Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.

JMIR AI·2025

Same author

Risk factors for intracerebral hemorrhage by five specific bleeding sites: Japan Public Health Center-based Prospective Study.

European stroke journal·2024

Same journal

The Finding of Posterior Wall Low-Voltage Zones During Cryoballoon Pulmonary Vein Isolation Facilitated by Periprocedural Electroanatomical Mapping Is Associated with a Worse Ablation Outcome.

Journal of cardiovascular development and disease·2026

Same journal

Autonomic Nervous Dysfunction and Ultra-Short-Term Heart Rate Variability in Atrial Fibrillation: Recent Advances in Early Detection.

Journal of cardiovascular development and disease·2026

Same journal

QRS-Corrected Prediction of the Diastolic Rest Period for Coronary CT Angiography in Patients with Complete Left Bundle Branch Block.

Journal of cardiovascular development and disease·2026

Same journal

Colchicine in Coronary Artery Disease-Too Much of a Good Thing?

Journal of cardiovascular development and disease·2026

Same journal

ANO1 (TMEM16A) Genetic Variants, Promoter Methylation, and Chloride Dysregulation in Pulmonary Hypertension.

Journal of cardiovascular development and disease·2026

Same journal

Prognostic Value of the Uric Acid-to-Albumin Ratio in Patients Undergoing Successful Percutaneous Coronary Intervention for Chronic Total Occlusion.

Journal of cardiovascular development and disease·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 15, 2026

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

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

Published on: January 11, 2020

Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study.

Thien Vu^1,2,3, Yoshihiro Kokubo², Mai Inoue^1,2

¹Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan.

Journal of Cardiovascular Development and Disease

|July 26, 2024

Summary

This summary is machine-generated.

Machine learning accurately predicts stroke risk and identifies key factors like age and blood pressure. This approach also reveals novel biomarkers, improving stroke prediction and risk assessment.

Keywords:

Shapley Additive Explanations (SHAP)extreme gradient boost (XGBoost)k-prototype clustering light gradient boosted machine (LightGBM)logistic regression random forest stroke supervised machine learning support vector machine (SVM)unsupervised machine learning

More Related Videos

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

The Mouse Stroke Unit Protocol with Standardized Neurological Scoring for Translational Mouse Stroke Studies

The Mouse Stroke Unit Protocol with Standardized Neurological Scoring for Translational Mouse Stroke Studies

Published on: February 7, 2025

Related Experiment Videos

Last Updated: Jun 15, 2026

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

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

Published on: January 11, 2020

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

The Mouse Stroke Unit Protocol with Standardized Neurological Scoring for Translational Mouse Stroke Studies

The Mouse Stroke Unit Protocol with Standardized Neurological Scoring for Translational Mouse Stroke Studies

Published on: February 7, 2025

Area of Science:

* Medical Informatics
* Public Health
* Cardiovascular Research

Background:

* Stroke is a major cause of death and disability worldwide.
* Accurate prediction and identification of risk factors are crucial for public health interventions.
* Machine learning offers advanced analytical capabilities for complex health data.

Purpose of the Study:

* To evaluate machine learning algorithms for stroke prediction.
* To identify key demographic, clinical, and novel biomarkers associated with stroke risk.
* To explore disparities in stroke incidence across different risk clusters.

Main Methods:

* Utilized the Suita study dataset (7389 participants, 53 variables).
* Employed unsupervised k-prototype clustering for risk stratification.
* Applied supervised models: Logistic Regression, Random Forest, SVM, XGBoost, LightGBM for prediction.
* Used Shapley Additive Explanations (SHAP) for feature importance analysis.

Main Results:

* Significant stroke incidence disparities were found among risk clusters.
* Random Forest demonstrated superior performance in stroke outcome prediction.
* Key predictors identified: age, systolic blood pressure, hypertension, eGFR, metabolic syndrome, blood glucose.
* Novel potential biomarkers: elbow joint thickness, fructosamine, hemoglobin, calcium levels.

Conclusions:

* Machine learning provides a robust framework for accurate stroke risk prediction.
* Identified both established and novel biomarkers for enhanced stroke risk assessment.
* Findings support data-driven approaches for early detection and prevention strategies.