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Related Experiment Video

Updated: Mar 1, 2026

Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model

Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model

Published on: August 11, 2023

Mining association rules between stroke risk factors based on the Apriori algorithm.

Qin Li, Yiyan Zhang, Hongyu Kang

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|June 7, 2017

Summary

This summary is machine-generated.

This study identified key stroke risk factors using the Apriori algorithm. Understanding these associations can guide early interventions to effectively reduce stroke incidence.

Keywords:

Apriori algorithm association rules risk factors stroke

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Published on: November 14, 2017

Area of Science:

Epidemiology
Data Mining
Public Health

Background:

Stroke is a prevalent and serious health concern.
Identifying stroke risk factors is crucial for prevention strategies.

Purpose of the Study:

To explore associations between various stroke risk factors.
To apply data mining techniques for discovering these relationships.

Main Methods:

Utilized the Apriori algorithm to analyze association rules.
Surveyed and conducted laboratory examinations on individuals aged 40+.
Grouped and filtered association rules based on confidence levels.

Main Results:

Analyzed 985,325 samples, identifying 15,835 stroke cases (1.65%).
Discovered eight significant association rules between stroke and its high-risk factors.
Identified 25 meaningful association rules among high-risk factors themselves.

Conclusions:

Meaningful association rules between stroke risk factors were established using the Apriori algorithm.
This approach offers a viable strategy for early intervention to mitigate stroke risk.