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

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作者
Lee Hwangbo
Yoon Jung Kang
Hoon Kwon
Jae Il Lee
Han-Jin Cho
Jun-Kyeung Ko
Sang Min Sung
Tae Hong Lee
机构
[1] Pusan National University Hospital,Department of Radiology
[2] Pusan National University Hospital,Department of Neurology
[3] Pusan National University Hospital,Department of Neurosurgery
[4] Pusan National University Hospital,Biomedical Research Institute
[5] Pusan National University,College of Medicine
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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.
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