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

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

doi:10.3934/mbe.2021004

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 1: 69-91. doi: 10.3934/mbe.2021004

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

1.
Department of Computer & AI, University of Jeddah, Jeddah, 23890, Saudi Arabia
2.
Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, 13100, Muzaffarabad, Pakistan
3.
Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, 13230, Muzaffarabad, Pakistan

Received: 29 September 2020 Accepted: 03 November 2020 Published: 19 November 2020

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.
- multimodal features,
- empirical receiver operating characteristics,
- machine learning,
- congestive heart failure,
- decision tree,
- normal sinus rhythm,
- support vector machine
Citation: Lal Hussain, Wajid Aziz, Ishtiaq Rasool Khan, Monagi H. Alkinani, Jalal S. Alowibdi. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features[J]. Mathematical Biosciences and Engineering, 2021, 18(1): 69-91. doi: 10.3934/mbe.2021004

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Abstract

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

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