Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning

Romina Torres, Christopher Zurita, Diego Mellado, Orietta Nicolis, Carolina Saavedra, Marcelo Tuesta, Matías Salinas, Ayleen Bertini, Oneglio Pedemonte, Marvin Querales, Rodrigo Salas

Research output: Contribution to journalArticlepeer-review


Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of 0.03±0.013 and an R2 of 63±19%, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an R2 up to 92%. The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase.

Original languageEnglish
Article number508
Issue number3
Publication statusPublished - 30 Jan 2023


  • cardiovascular rehabilitation
  • joint distribution adaptation
  • machine learning
  • transfer feature learning

ASJC Scopus subject areas

  • Clinical Biochemistry


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