Machine learning based congestive heart failure detection using feature importance ranking of multimodal features.

Hussain, Lal; Aziz, Wajid; Khan, Ishtiaq Rasool; Alkinani, Monagi H; Alowibdi, Jalal S

Hussain, Lal; Aziz, Wajid; Khan, Ishtiaq Rasool; Alkinani, Monagi H; Alowibdi, Jalal S.

Affiliation

Hussain L; Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, 13100, Muzaffarabad, Pakistan.
Aziz W; Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, 13230, Muzaffarabad, Pakistan.
Khan IR; Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia.
Alkinani MH; Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia.
Alowibdi JS; Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia.

Math Biosci Eng ; 18(1): 69-91, 2020 11 19.

Article in En | MEDLINE | ID: mdl-33525081

Subject(s)

Heart Failure; Machine Learning; Algorithms; Bayes Theorem; Heart Failure/diagnosis; Humans; ROC Curve; Support Vector Machine

Key words

congestive heart failure; decision tree; empirical receiver operating characteristics; machine learning; multimodal features; normal sinus rhythm; support vector machine

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning / Heart Failure Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Math Biosci Eng Year: 2020 Document type: Article Affiliation country: Pakistan

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