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Stroke is a critical medical condition caused by blockage (ischemic stroke) or rupture (hemorrhagic stroke) of blood vessels in the brain. However, stroke research and diagnosis are advancing, requiring ongoing studies to enhance understanding and management. The goal is to refine prevention strategies, develop effective treatment options, and improve patient outcomes. Continued research aims to improve prevention, early detection, and treatment decisions. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and XGBoost, have been used to analyze data and detect stroke. XGBoost achieved the highest accuracy among them all. The field seeks to further enhance our knowledge and drive advancements to address this critical condition.

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Examining the Accuracy of Machine Learning Classification Models in Stroke Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • M. Nazma Naskar,
  • Abhinav Prakash,
  • Vanshita Sinha,
  • Harshita Kumari,
  • Anirban Bhattacharjee

摘要

Stroke is a critical medical condition caused by blockage (ischemic stroke) or rupture (hemorrhagic stroke) of blood vessels in the brain. However, stroke research and diagnosis are advancing, requiring ongoing studies to enhance understanding and management. The goal is to refine prevention strategies, develop effective treatment options, and improve patient outcomes. Continued research aims to improve prevention, early detection, and treatment decisions. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and XGBoost, have been used to analyze data and detect stroke. XGBoost achieved the highest accuracy among them all. The field seeks to further enhance our knowledge and drive advancements to address this critical condition.