Accuracy, precision, sensitivity, F1-score, and specificity for different classifiers using XGBoost and LASSO as feature extractors.

Accuracy, precision, sensitivity, F1-score, and specificity for different classifiers using XGBoost and LASSO as feature extractors.

<p>Accuracy, precision, sensitivity, F1-score, and specificity for different classifiers using XGBoost and LASSO as feature extractors.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Armin Abdollahi (21440129) (author)
مؤلفون آخرون:	Negin Ashrafi (651043) (author), Xinghong Ma (199646) (author), Jiahao Zhang (4859203) (author), Daijia Wu (21440132) (author), Tongshou Wu (21440135) (author), Zizheng Ye (21440138) (author), Maryam Pishgar (16527234) (author)
منشور في:	2025
الموضوعات:	Medicine Genetics Biotechnology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified selected 30 features results </ b optimizing treatment strategies methods </ b intensive care unit death among adults conclusion </ b background </ b applied synthetic minority advanced predictive modeling 70 %, n 15 %, n reducing feature number enhanced mortality prediction assess mortality risk 2441 ), test proposed model demonstrates address data imbalances chose deep learning enhanced feature selection auroc improvement compared deep learning model ischemic stroke patients deep learning feature selection predicting mortality stroke patients ischemic strokes model ’ model development dl model data cleaning acquired data vital signs stroke annually smote ). sampling technique randomly divided previous studies leading cause laboratory tests iv database including diagnoses first day existing models combining xgboost clinical notes best study auroc improved allocating resources
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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