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A machine learning approach to select features important to stroke prognosis.

Gang Fang1, Wenbin Liu1, Lixin Wang2

  • 1Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China.

Computational Biology and Chemistry
|July 7, 2020
PubMed
Summary

Machine learning identified key prognostic factors for ischemic stroke, improving long-term disability prediction. These selected features accurately predict infarcts visible on CT scans, aiding in understanding stroke causes like large artery occlusion.

Keywords:
Feature SelectionISTIschemic strokeMachine learning

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Area of Science:

  • Neurology
  • Medical Informatics
  • Machine Learning

Background:

  • Ischemic stroke is a leading cause of long-term disability globally.
  • Effective intervention and treatment rely on identifying stroke prognosis factors.
  • Predicting stroke outcomes remains challenging in clinical practice.

Purpose of the Study:

  • To apply an integrated machine learning approach for selecting stroke prognosis features.
  • To identify robust features for predicting infarcts visible on CT (RVISINF).
  • To explore the relationship between selected features and large artery occlusion (LAO).

Main Methods:

  • Feature importance was ranked using Shapiro-Wilk and Pearson correlations.
  • Recursive Feature Elimination with Cross-Validation (RFECV) was employed with multiple classifiers.
  • Feature selection was validated using Random-Forest-Classifier and Shapiro-Wilk algorithm.
  • Support Vector Classification (SVC), Multi-Layer Perceptron (MLP), Random Forest, and AdaBoost were used for prediction.

Main Results:

  • Twenty-three robust features were selected for stroke prognosis.
  • The selected features achieved high accuracy in predicting RVISINF in acute stroke patients.
  • The study identified factors associated with RVISINF, linked to LAO.

Conclusions:

  • The identified features can accurately infer long-term prognosis of acute ischemic stroke.
  • These features aid in understanding factors related to RVISINF and LAO.
  • This machine learning approach offers a valuable tool for stroke outcome prediction and analysis.