Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization

Abstract

Introduction The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders. Methods We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response. Results We compared the performance of EoE against traditional ML methods and the state‐of‐the‐art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1‐score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results. Conclusion The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high‐risk patients.

Artificial Intelligence in Heart Failure: Friend or Foe?

“…In a retrospective trial of 1664 patients, Cai et al attempted to identify CRT responders using contemporary methods of ML [42]. An ensemble of ensemble (EOE) method was the model of ML that was used in order to create supervised and unsupervised layers of stratification.…”

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

“…The distribution of the ML-based CVD studies with and without feature selection are shown in Figure 2c. It was found that almost 82% of MLbased CVD studies performed feature selection for risk prediction whereas only 18% [69,70,73,75,83,94,96,110,120] did not perform it. For the ML-based multi-label CVD (Figure 2d), the total number of GT's used for each study were as follows and given in the ground braces: Venkatesh et al The percentage of studies for each of the three kinds of CVD risk prediction had the following distributions: multiclass (26%) [69][70][71][72][73][74][75][76][77][78][79][80][81][82], multi-label (15%) [83][84][85][86][87][88][89][90], and ensemble (59%) [80, (Figure 2a).…”

“…The distribution of the ML-based CVD studies with and without feature selection are shown in Figure 2c. It was found that almost 82% of ML-based CVD studies performed feature selection for risk prediction whereas only 18% [69,70,73,75,83,94,96,110,120] did not perform it. For the ML-based multi-label CVD (Figure 2d), the total number of GT's used for each study were as follows and given in the ground braces: Venkatesh et al 4) [90].…”

“…The pseudo-code that represents the ensemble-based risk stratification process can be seen in Appendix C. The ensemble technique can be applied to the CVD field, as well as to other fields, such as education, Alzheimer's, etc. The different classifiers used in ensemble techniques were kNN, Reglog, GaussNB (GNB), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), random forest (RF) [91,[95][96][97][98], multilayer perceptron (MLP), SVM [91,94,95,97,101,103,104], CNN, long short term memory network (LSTM), gated recurrent unit (GRU), bidirectional LSTM, bidirectional GRU [92], bagging, XGBoost, Adaboost [93,99], DNN [94], generalized additive models (GAMs), elastic net, penalized logistic regression (PLR), gradient boosted machines (GBMs), Bayesian logistic regression [96], K-NN [98,99,102,104,121], NB [101,104], light GBM, GBDT, LR, BPNN, DT [98,99,104,109], GB [99], Adaboost ensemble [100], ANN [101,104], GNB, LDA, LR, QDA, AdaBoost [105,113,118], XGBoost [102,118], ensemble SVM [104], CART [106], bagging, VS, LASSO, boosting, Bassian, MARS, logistic [107], ensemble boosting...…”

“…The range of performance evaluation parameters used for the multiclass, multi-label, and ensemble was 1-5, 1-8, and 1-8, respectively. The different types of classifiers used for these three techniques were SVM [91,94,95,97,101,103,104], RF [91,[95][96][97][98], CNN, DT, k-NN, Agatston classifier, Elastic Net, NN, NB, XGBoost, SVM, ELM, one against one (OAO), one against all (OAA), decision direct acyclic graph (DDAG), exhaustive output error correction code (ECOC) [69][70][71][72][73][74][75][76][77][78][79][80][81][82]. The power analysis is also done on more multi-label and ensemble-based techniques.…”