استفاده از روش های مبتنی برژنتیک در پیش بینی زمان بین شروع علایم تا رسیدن به بیمارستان در بیماران استروکی و ریسک فاکتورهای مرتبط

عبدالهی, جعفر and امانی, فیروز and محمدنیا, علیرضا and امانی, پانیذ and فتاح زاده اردلانی, قاسم (1400) استفاده از روش های مبتنی برژنتیک در پیش بینی زمان بین شروع علایم تا رسیدن به بیمارستان در بیماران استروکی و ریسک فاکتورهای مرتبط. Journal of Biostatistics and Epidemiology ــ 8 (1). pp. 8-23. شاپا 2383-4196

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Official URL: https://jbe.tums.ac.ir/index.php/jbe/article/view/...


Using Stacking methods based Genetic Algorithm to predict the time between symptom onset and hospital arrival in stroke patients and its related factors

English Abstract

Introduction: Early arrival of patients with acute ischemic stroke to start of treatment by recombinant tissue plasminogen activator (rt-PA) within 4.5 hours after onset of stroke. We aimed to develop a machine learning method to predict effective factors on arrival time of patients with stroke to hospital after symptom onset. Methods: We included 676 patients with ischemic stroke who referred to Ardabil city hospital a province in northwest of Iran at year 2018. Classification models such as Random forest (RF), Gradient Boosting Classifier (GB), Decision Tree Classifier (DT), Support-Vector Machines (SVM), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) with 10-fold cross-validation were developed to predict effective factors on arrival time of patient with stroke to hospital. The performances were evaluated with accuracy, sensitivity, specificity, positive prophetical worth, and negative prophetical worth. Results: Of all patients, 25.3% arrived to the hospital in less than 4.5 hours. The accuracy of RF, NB, ANN, GB, DT, SVM, LR and suggest method (Stacking) were 0.98, 0.72, 0.73, 0.79, 0.98, 0.73, 0.74, and 0.99. Conclusion: In this study, the Stacking technique provide a better result (Accuracy 99.51%, sensitivity 100%, and specificity 99.40%) among all other techniques and this model could be used as a valuable tool for clinical decision making.

Item Type:Article
زبان سند : انگلیسی
نویسنده اول :جعفر عبدالهی
نویسنده مسئول :فیروز امانی
نویسنده :علیرضا محمدنیا
نویسنده :پانیذ امانی
نویسنده :قاسم فتاح زاده اردلانی
Additional Information:Indexed in: Scopus, DOAJ , Google Scholar, ISC, SID, Magiran
کلیدواژه ها (انگلیسی):Stroke, Machine learning, Classification, Random Forest, Hospital
Subjects:WL Nervous system
WT Geriatrics . Chronic Diseases
Divisions:Faculty of Medicine > Department of Pediatrics , Community Medicine
Faculty of Medicine > Department of Basic Sciences > Department of Health Information Management
Faculty of Medicine > Department of Dermatology , Psychiatry , Neurology
ID Code:15460
Deposited By: Dr Firouz Amani
Deposited On:10 Feb 1401 12:03
Last Modified:10 Feb 1401 12:03

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