Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques.

The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal dynamics. Based on these characteristics, we extracted multimodal features from congestive heart failure (CHF) and normal sinus rhythm (NSR) signals. We performed the synthetic minority over-sampling technique (SMOTE) to increase the number of CHF subjects to balance our train data. The classification between these subjects with original data and SMOTE data was performed using machine learning classifiers such as classification and regression tree (CART), support vector machine linear (SVM-L), Naïve Bayes, neural network, and ensemble classifiers such as random forest (RF), XG boost, averaged neural network (AVNNET). With the original data, the highest performance was obtained using SVM-L with accuracy (94.28%), sensitivity (84.61%), specificity (100%), p-value (0.0002), AUC (0.9605) with 95% CI: 0.9006-1.00. By applying the SMOET, the highest performance was obtained with SVM-L with accuracy (97.14%), sensitivity (92.30%), specificity (100%), p-value (7.99e-06), AUC (0.9650) with 95% CI: 0.8945–1.00. The results reveal that proposed approach with SMOTE improved the detection performance which can be very effective and computationally efficient tool for automatic detection of congestive heart failure patients. [ABSTRACT FROM AUTHOR]