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

Cheng Cai, Ahmad P. Tafti, Che Ngufor, Pei Zhang, Peilin Xiao, Mingyan Dai, Hongfang Liu, Peter Noseworthy, Minglong Chen, Paul A. Friedman, Yong Mei Cha

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2504-2514
Number of pages11
JournalJournal of cardiovascular electrophysiology
Issue number9
StatePublished - Sep 2021


  • artificial intelligence
  • cardiac resynchronization therapy
  • heart failure
  • machine learning
  • prediction

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)


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