A Machine Learning Approach for Chronic Heart Failure Diagnosis

Dafni K. Plati, Evanthia E. Tripoliti, Aris Bechlioulis, Aidonis Rammos, Iliada Dimou, Lampros Lakkas, Chris Watson, Ken McDonald, Mark Ledwidge, Rebabonye Pharithi, Joe Gallagher, Lampros K. Michalis, Yorgos Goletsis, Katerina K. Naka, Dimitrios I. Fotiadis*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
139 Downloads (Pure)


The aim of this study was to address chronic heart failure (HF) diagnosis with the application of machine learning (ML) approaches. In the present study, we simulated the procedure that is followed in clinical practice, as the models we built are based on various combinations of feature categories, e.g., clinical features, echocardiogram, and laboratory findings. We also investigated the incremental value of each feature type. The total number of subjects utilized was 422. An ML approach is proposed, comprising of feature selection, handling class imbalance, and classification steps. The results for HF diagnosis were quite satisfactory with a high accuracy (91.23%), sensitivity (93.83%), and specificity (89.62%) when features from all categories were utilized. The results remained quite high, even in cases where single feature types were employed.
Original languageEnglish
Article number1863
Issue number10
Publication statusPublished - 10 Oct 2021


  • heart failure
  • machine learning


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