Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.

Rahman, Md Sohanur; Rahman, Hasib Ryan; Prithula, Johayra; Chowdhury, Muhammad E H; Ahmed, Mosabber Uddin; Kumar, Jaya; Murugappan, M; Khan, Muhammad Salman

Rahman, Md Sohanur; Rahman, Hasib Ryan; Prithula, Johayra; Chowdhury, Muhammad E H; Ahmed, Mosabber Uddin; Kumar, Jaya; Murugappan, M; Khan, Muhammad Salman.

Affiliation

Rahman MS; Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Rahman HR; Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Prithula J; Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Chowdhury MEH; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.
Ahmed MU; Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Kumar J; Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.
Murugappan M; Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait.
Khan MS; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.

Diagnostics (Basel) ; 13(11)2023 Jun 02.

Article in En | MEDLINE | ID: mdl-37296800

Key words

electronic health data; emergency readmission; heart failure; machine learning; stacking classification

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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Aspects: Patient_preference Language: En Journal: Diagnostics (Basel) Year: 2023 Document type: Article Affiliation country: Bangladesh Country of publication: Switzerland

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