Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.

Journal: Cardiovascular engineering and technology
PMID: 38782877

Abstract

PURPOSE: To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms.

Authors

  • Jing Liao
    State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Kouichi Misaki
    Department of Neurosurgery, Kanazawa University, Ishikawa, Japan.
  • Jiro Sakamoto
    Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa, 920-1192, Japan. Electronic address: sakamoto@se.kanazawa-u.ac.jp.

Keywords

External Resources

Popular Topics
Recent Journals