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

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Predicting Stroke Risk Using Machine Learning: A Data-Driven Approach to Early Detection and Prevention.

Muhammed Sutcu¹, Dana Jouda¹, Baris Yildiz²

¹Gulf University for Science and Technology (GUST), GUST Engineering and Applied Innovation Research Center (GEAR), Department of Electrical and Computer Engineering, Hawally, Kuwait.

Stroke Research and Treatment

|November 25, 2025

Summary

This summary is machine-generated.

Predicting stroke risk is crucial for early intervention. Key factors include age, glucose, BMI, hypertension, and heart disease, with machine learning models aiding identification of high-risk individuals.

Keywords:

XGBoost clustering early detection feature importance naïve Bayes predicting stroke risk using machine learning stroke prevention survival analysis

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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

Area of Science:

Neurology
Public Health
Data Science

Background:

Stroke is a leading cause of death and disability globally.
Early risk prediction and intervention are essential for mitigating stroke's impact.

Purpose of the Study:

To identify key predictors of stroke using statistical and machine learning methods.
To analyze stroke risk factors in a large dataset of 5110 individuals.

Main Methods:

Employed statistical analysis, machine learning (classification, clustering, survival modeling).
Utilized Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) for clustering.
Evaluated machine learning models including XGBoost and Naïve Bayes.

Main Results:

Identified age, glucose levels, BMI, hypertension, and heart disease as primary stroke risk factors.
Stroke prevalence is significantly higher in patients with hypertension (13.25%) and heart disease (17.03%).
XGBoost demonstrated a strong performance in predicting stroke, while Naïve Bayes maximized case detection.

Conclusions:

Findings highlight the critical role of early screening and lifestyle interventions.
Hypertension and advanced age (over 60) are associated with increased stroke risk.
Further research should focus on data balancing techniques and real-time clinical decision support tools.