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

You might also read

Related Articles

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

Sort by
Same author

Advances in Neurological Diseases: Pathogenesis, Diagnosis and Therapeutic Strategies.

Biomedicines·2026
Same author

Agreement Between Standing Eight-Point Multifrequency Bioelectrical Impedance Analysis and Dual-Energy X-Ray Absorptiometry for Body Composition Assessment in Apparently Healthy Greek Adults.

Healthcare (Basel, Switzerland)·2026
Same author

Physical Activity During Official Match Play in Female Masters Basketball Players: An Accelerometry-Based Study.

Sports (Basel, Switzerland)·2026
Same author

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

BMJ open·2026
Same author

Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review.

Journal of functional morphology and kinesiology·2026
Same author

Exploring Usability and User Engagement in Fully Immersive Virtual Reality Systems for Upper Limb Stroke Rehabilitation: A Scoping Review.

Progress in rehabilitation medicine·2026

Related Experiment Video

Updated: Aug 23, 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

8.3K

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data.

Christos Kokkotis1, Georgios Giarmatzis1, Erasmia Giannakou1

  • 1Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece.

Diagnostics (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

Artificial intelligence (AI) models can predict stroke risk by analyzing patient data. Machine learning, specifically the Multi-Layer Perceptron classifier, shows promise in identifying individuals at high risk for stroke.

Keywords:
clinical datainterpretationmachine learningprognosisstroke

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K
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

7.6K

Related Experiment Videos

Last Updated: Aug 23, 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

8.3K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K
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

7.6K

Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Stroke is a leading cause of death and disability worldwide, necessitating improved risk prediction.
  • Existing methods struggle with the class imbalance inherent in stroke patient data.
  • Accurate modeling of stroke risk factors is crucial for effective prevention and treatment.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting stroke occurrence.
  • To address the challenge of class imbalance in stroke prediction datasets.
  • To interpret ML model decisions to understand the influence of risk factors.

Main Methods:

  • Developed and compared six ML classifiers for stroke prediction.
  • Utilized the Multi-Layer Perceptron (MLP) classifier, achieving an 18.60% false-negative rate.
  • Employed Shapley Additive Explanations (SHAP) for model interpretability and risk factor analysis.

Main Results:

  • The MLP classifier demonstrated superior performance in minimizing false negatives.
  • SHAP analysis provided insights into the impact of various risk factors on stroke prediction.
  • The ML approach effectively handled the class imbalance issue in stroke data.

Conclusions:

  • AI-powered ML models, particularly MLP, offer a reliable method for stroke risk prediction.
  • Interpretable AI facilitates understanding of stroke risk factors, aiding clinical decision-making.
  • This approach can enhance stroke risk stratification strategies for timely diagnosis and treatment.