Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques.
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular syste...
| Publicado en: | BioMed Research International pp. 1 - 20 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/18/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141922434&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141922434 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/18/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141922434 141922434 141922434 10.1155/2020/4281243 141922434 ppf: 1 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. aug: au: Hussain, Lal Awan, Imtiaz Ahmed Aziz, Wajid Saeed, Sharjil Ali, Amjad Zeeshan, Farukh Kwak, Kyung Sup affil: Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100 Muzaffarabad, Azad Kashmir, Pakistan sug: subj: Automation Machine Learning Heart Failure Diagnosis Human Models, Statistical Decision Trees Sensitivity and Specificity Predictive Value of Tests ROC Curve Confidence Intervals ab: The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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