Application of unsupervised learning in weight-loss categorisation for weight management programs

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Oladapo Babajide, Tawfik Hissam, Anna Palczewska, Arne Astrup, J Alfredo Martinez, Jean Michel Oppert, Thorkild I.A. Sørensen

There has been an increase in the need to have a weight management system that prevents adverse health conditions which can in the future lead to various
cardiovascular diseases. Several types of research were made in attempting to understand and better manage body-weight gain and obesity.

This study focuses on a data-driven approach to identify patterns in profiles with body-weight change in a dietary intervention program using machine learning algorithms. The proposed line of investigation would analyse these patient’s profile at the entry of dietary intervention program and for some, on a weekly basis. These attributes would serve as inputs into machine learning algorithms.

From the unsupervised learning perspective, the paper seeks to address the first stage in applying machine learning algorithms to weight management data. The specific aim here is to identify the thresholds for weight loss categories which
are required for supervised learning.

OriginalsprogEngelsk
TitelThe 10th IEEE International Conference on Dependable Systems, Services and Technologies : DESSERT'2019
ForlagIEEE
ISBN (Trykt)978-1-7281-1733-1
StatusE-pub ahead of print - 19 jun. 2019
BegivenhedIEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019 - Leeds Beckett University, Leeds, Storbritannien
Varighed: 5 jun. 20197 jun. 2019
Konferencens nummer: 10
http://dessert.ieee.org.ua/dessert-2019/program/

Konference

KonferenceIEEE International Conference on Dependable Systems, Services and Technologies
Nummer10
LokationLeeds Beckett University
LandStorbritannien
ByLeeds
Periode05/06/201907/06/2019
Internetadresse

Bibliografisk note

CURIS 2019 NEXS 210

ID: 222747272