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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationThe 10th IEEE International Conference on Dependable Systems, Services and Technologies : DESSERT'2019
PublisherIEEE
Publication date19 Jun 2019
ISBN (Print)978-1-7281-1733-1
Publication statusE-pub ahead of print - 19 Jun 2019
EventIEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019 - Leeds Beckett University, Leeds, United Kingdom
Duration: 5 Jun 20197 Jun 2019
Conference number: 10
http://dessert.ieee.org.ua/dessert-2019/program/

Conference

ConferenceIEEE International Conference on Dependable Systems, Services and Technologies
Nummer10
LocationLeeds Beckett University
LandUnited Kingdom
ByLeeds
Periode05/06/201907/06/2019
Internetadresse

    Research areas

  • The Faculty of Science - Weight management, Weight loss categorisation, Unsupervised learning, Data clustering, Smart health management

ID: 222747272