Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke.

Computers in Biology and Medicine
N KappelhofH A Marquering

Abstract

Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on avera...Continue Reading

References

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Oct 13, 2020·Stroke; a Journal of Cerebral Circulation·Gianluca BrugnaraPhilipp Vollmuth
Nov 13, 2020·Frontiers in Neurology·Lucas A RamosHenk A Marquering

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