Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients

Patients with acute ischemic stroke can benefit from reperfusion therapy. Nevertheless, there are gray areas where initiation of reperfusion therapy is neither supported nor contraindicated by the current practice guidelines. In these situations, a prediction model for mortality can be beneficial in...

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Bibliographic Details
Published in:Scientific reports 2022-10, Vol.12 (1), p.17389-17389, Article 17389
Main Authors: Hwangbo, Lee, Kang, Yoon Jung, Kwon, Hoon, Lee, Jae Il, Cho, Han-Jin, Ko, Jun-Kyeung, Sung, Sang Min, Lee, Tae Hong
Format: Article
Language:English
Subjects:
631/114/1314
631/114/1751
Bayes Theorem
Bayesian analysis
Decision making
Humanities and Social Sciences
Humans
Ischemia
Ischemic Stroke - therapy
Mortality
multidisciplinary
Neural networks
Neural Networks, Computer
Patients
Prediction models
Reperfusion
ROC Curve
Science
Science (multidisciplinary)
Stroke
Support Vector Machine
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Summary:Patients with acute ischemic stroke can benefit from reperfusion therapy. Nevertheless, there are gray areas where initiation of reperfusion therapy is neither supported nor contraindicated by the current practice guidelines. In these situations, a prediction model for mortality can be beneficial in decision-making. This study aimed to develop a mortality prediction model for acute ischemic stroke patients not receiving reperfusion therapies using a stacking ensemble learning model. The model used an artificial neural network as an ensemble classifier. Seven base classifiers were K-nearest neighbors, support vector machine, extreme gradient boosting, random forest, naive Bayes, artificial neural network, and logistic regression algorithms. From the clinical data in the International Stroke Trial database, we selected a concise set of variables assessable at the presentation. The primary study outcome was all-cause mortality at 6 months. Our stacking ensemble model predicted 6-month mortality with acceptable performance in ischemic stroke patients not receiving reperfusion therapy. The area under the curve of receiver-operating characteristics, accuracy, sensitivity, and specificity of the stacking ensemble classifier on a put-aside validation set were 0.783 (95% confidence interval 0.758–0.808), 71.6% (69.3–74.2), 72.3% (69.2–76.4%), and 70.9% (68.9–74.3%), respectively.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-22323-9