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Predicting adherence of patients with HF through machine learning techniques

Content Provider	IET Digital Library
Author	Karanasiou, Georgia Spiridon Tripoliti, Evanthia Eleftherios Papadopoulos, Theofilos Grigorios Kalatzis, Fanis Georgios Goletsis, Yorgos Naka, Katerina Kyriakos Bechlioulis, Aris Errachid, Abdelhamid Fotiadis, Dimitrios Ioannis
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.
Starting Page	165
Ending Page	170
Page Count	6
Volume Number	3
e-ISSN	20533713
Issue Number	Issue 3, Sep (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/htl/3/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/htl.2016.0041
Journal	Healthcare Technology Letters
Publisher Date	2016-09-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cardiology Chronic Disease Diseases Heart Failure Learning in AI Machine Learning Technique Medication Nutrition Patient Adherence Prediction Patient Care And Treatment Patient Monitoring Patient Treatment Physical Activity
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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