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Updated: May 20, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Harnessing the hybrid machine learning methods for stroke risk classification.

Dongxian Yu¹, Mingjie Wang²

¹College of Modern Information Technology, Henan Polytechnic, Zhengzhou, Henan, China.

Computer Methods in Biomechanics and Biomedical Engineering

|May 19, 2025

Summary

This summary is machine-generated.

This study uses artificial intelligence (AI) and machine learning (ML) to predict stroke risk. A hybrid AI model achieved over 0.985 accuracy, showing promise for early stroke detection and prevention.

Keywords:

Stroke prediction artificial intelligence hist Gradient Boosting hybrid classifier model machine learning risk factors

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

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Published on: January 11, 2020

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Cardiovascular Disease Research

Background:

Stroke is a primary cause of mortality worldwide.
A significant majority of stroke cases are preventable.
Early detection and awareness are crucial for stroke prevention.

Purpose of the Study:

To develop and evaluate AI and ML models for predicting stroke risk.
To identify key risk factors contributing to stroke.
To assess the performance of various ML algorithms in stroke prediction.

Main Methods:

Utilized a dataset of 5,110 records with risk factors like age, hypertension, and heart disease.
Evaluated multiple machine learning models: Gradient Boosting, Hist Gradient Boosting, AdaBoost, and Decision Trees.
Developed a hybrid model combining Hist Gradient Boosting with optimizer algorithms.

Main Results:

The hybrid AI model demonstrated superior performance in stroke risk prediction.
Achieved accuracy, precision, recall, and F1-score exceeding 0.985.
Indicated high efficacy of the developed model in identifying individuals at risk of stroke.

Conclusions:

AI and ML models, particularly the hybrid approach, show significant potential for accurate stroke risk prediction.
Effective stroke prediction can enhance early detection and preventative strategies.
This research contributes to advancing AI applications in public health for cardiovascular disease management.