Key result
Machine learning using all multimodal features with an SVM Gaussian classifier achieved the highest detection performance for congestive heart failure, with an accuracy of 88.79% and an AUC of 0.95.
Why the study?
Does machine learning using ranked multimodal features accurately detect congestive heart failure?
Does machine learning using ranked multimodal features accurately detect congestive heart failure?
Effect estimate: AUC 0.95
Machine learning models, particularly SVM Gaussian using multimodal features, can accurately detect congestive heart failure.
Multimodal ML classifiers may aid CHF detection research; leaves open clinical adoption pending prospective validation.
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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Hussain et al. (2020) studied Congestive Heart Failure. Machine learning based detection using multimodal features vs. Normal Sinus Rhythm (NSR) subjects was evaluated on Detection of Congestive Heart Failure (Accuracy and AUC) (AUC 0.95). Machine learning using all multimodal features with an SVM Gaussian classifier achieved the highest detection performance for congestive heart failure, with an accuracy of 88.79% and an AUC of 0.95.
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