Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques
المؤلفون المشاركون
Kwak, Kyungsup
Ali, Amjad
Hussain, Lal
Awan, Imtiaz Ahmed
Aziz, Wajid
Saeed, Sharjil
Zeeshan, Farukh
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-18
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hussain, Lal& Awan, Imtiaz Ahmed& Aziz, Wajid& Saeed, Sharjil& Ali, Amjad& Zeeshan, Farukh…[et al.]. 2020. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. BioMed Research International،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1133842
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hussain, Lal…[et al.]. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. BioMed Research International No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1133842
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hussain, Lal& Awan, Imtiaz Ahmed& Aziz, Wajid& Saeed, Sharjil& Ali, Amjad& Zeeshan, Farukh…[et al.]. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1133842
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1133842
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر