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Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
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Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in

Josline Adhiambo Otieno1, Jenny Häggström1, David Darehed2

  • 1Department of Statistics, USBE, Umeå University, Umeå, Sweden.

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Summary

Machine learning models like artificial neural networks (ANN) and eXtreme Gradient Boosting (XGBoost) show improved stroke outcome prediction. These advanced models enhance the ability to forecast functional dependence and death after stroke.

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Area of Science:

  • Neurology
  • Data Science
  • Public Health

Background:

  • Stroke is a leading cause of death and disability globally, imposing significant societal costs.
  • Accurate stroke outcome prediction is crucial for optimizing healthcare resource allocation and improving patient care.
  • Existing prediction methods may not fully leverage the potential of routinely collected data.

Purpose of the Study:

  • To develop and evaluate supervised machine learning models for predicting stroke outcomes (functional dependence and death).
  • To compare the performance and explainability of artificial neural networks (ANN), eXtreme Gradient Boosting (XGBoost), and support vector machines against traditional multinomial logistic regression (mLR).
  • To utilize routinely-collected data from the Swedish Stroke Registry (Riksstroke) for prognostic modeling.

Main Methods:

  • A prognostic study using data from 102,135 adult patients in the Swedish Stroke Registry (2015-2020).
  • Predictive factors included demographics, pre-stroke status, risk factors, medications, acute care, stroke type, and severity.
  • Models evaluated: Support Vector Machines, ANN, XGBoost, and mLR, with predictions explained using SHAP values.

Main Results:

  • All models achieved similar overall accuracy (69%-70%).
  • ANN and XGBoost significantly outperformed mLR in predicting functional dependence, evidenced by higher F1-scores (0.603 and 0.577, respectively, vs. 0.544 for mLR).
  • Predictive factors identified were consistent across models and aligned with clinical knowledge.

Conclusions:

  • ANN and XGBoost models offer a modest improvement in predicting stroke outcomes and provide better explainability compared to mLR.
  • These advanced machine learning approaches, using routinely collected data, can enhance the prediction of functional dependence after stroke.
  • Improved prediction accuracy is vital for effective planning and organization of acute stroke care and rehabilitation services.