title: Predicting adherence of patients with HF through machine learning techniques.
abstract: Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques. Specifically, it aims to classify a patient not only as medication adherent or not, but also as adherent or not in terms of medication, nutrition and physical activity (global adherent). Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. About 11 classification algorithms are employed and combined with feature selection and resampling techniques. The classifiers are evaluated on a dataset of 90 patients. The patients are characterised as medication and global adherent, based on clinician estimation. The highest detection accuracy is 82 and 91% for the first and the second classification problem, respectively.
authors.affiliation: Department of Biomedical Research , Institute of Molecular Biology and Biotechnology , FORTH, GR 45110 Ioannina , Greece.
authors.forename: Georgia Spiridon
authors.initials: GS
authors.lastname: Karanasiou
authors.affiliation: Department of Biomedical Research , Institute of Molecular Biology and Biotechnology , FORTH, GR 45110 Ioannina , Greece.
authors.forename: Evanthia Eleftherios
authors.initials: EE
authors.lastname: Tripoliti
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems , University of Ioannina , GR 45110 Ioannina , Greece.
authors.forename: Theofilos Grigorios
authors.initials: TG
authors.lastname: Papadopoulos
authors.affiliation: Department of Biomedical Research , Institute of Molecular Biology and Biotechnology , FORTH, GR 45110 Ioannina , Greece.
authors.forename: Fanis Georgios
authors.initials: FG
authors.lastname: Kalatzis
authors.affiliation: Department of Economics , University of Ioannina , GR 45110 Ioannina , Greece.
authors.forename: Yorgos
authors.initials: Y
authors.lastname: Goletsis
authors.affiliation: Michaelidion Cardiac Center, University of Ioannina, GR 45110 Ioannina, Greece; Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece.
authors.forename: Katerina Kyriakos
authors.initials: KK
authors.lastname: Naka
authors.affiliation: Michaelidion Cardiac Center, University of Ioannina, GR 45110 Ioannina, Greece; Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece.
authors.forename: Aris
authors.initials: A
authors.lastname: Bechlioulis
authors.affiliation: Université de Lyon, Institut de Sciences Analytiques, ISA , FR 69100 Villeurbanne , France.
authors.forename: Abdelhamid
authors.initials: A
authors.lastname: Errachid
authors.affiliation: Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece; Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.
authors.forename: Dimitrios Ioannis
authors.initials: DI
authors.lastname: Fotiadis
keywords: cardiology; chronic disease; diseases; heart failure; learning (artificial intelligence); machine learning techniques; medication; nutrition; patient adherence prediction; patient monitoring; patient treatment; physical activity