Predicting adherence of patients with HF through machine learning techniques. Issue 3 (27th September 2016)

• Record Type: Journal Article
• Authors: Karanasiou, Georgia Spiridon; Tripoliti, Evanthia Eleftherios; Papadopoulos, Theofilos Grigorios; Kalatzis, Fanis Georgios; Goletsis, Yorgos; Naka, Katerina Kyriakos; Bechlioulis, Aris; Errachid, Abdelhamid; Fotiadis, Dimitrios Ioannis
• Is Part Of: Healthcare technology letters. Volume 3:Issue 3(2016)
• Page Start: 165
• Page End: 170
• Publication Date: 2016-09-27
• Subjects: patient treatment -- patient monitoring -- learning (artificial intelligence) -- cardiology -- diseases; patient adherence prediction -- heart failure -- machine learning techniques -- chronic disease -- patient monitoring -- medication -- nutrition -- physical activity; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2016.0041
• Languages: English
• ISSNs: 2053-3713

Abstract

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.