Open AccessPredicting adherence of patients with HF through machine learning techniques
Author(s) -
Karanasiou Georgia Spiridon,
Tripoliti Evanthia Eleftherios,
Papadopoulos Theofilos Grigorios,
Kalatzis Fanis Georgios,
Goletsis Yorgos,
Naka Katerina Kyriakos,
Bechlioulis Aris,
Errachid Abdelhamid,
Fotiadis Dimitrios Ioannis
Publication year - 2016
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2016.0041
Subject(s) - machine learning , feature selection , medicine , artificial intelligence , quality of life (healthcare) , heart failure , feature (linguistics) , computer science , intensive care medicine , linguistics , philosophy , nursing
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.