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

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

Standard

Application of unsupervised learning in weight-loss categorisation for weight management programs. / Babajide, Oladapo; Hissam, Tawfik; Palczewska, Anna; Astrup, Arne; Martinez, J Alfredo; Oppert, Jean Michel; Sørensen, Thorkild I.A.

The 10th IEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019. IEEE, 2019.

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

Harvard

Babajide, O, Hissam, T, Palczewska, A, Astrup, A, Martinez, JA, Oppert, JM & Sørensen, TIA 2019, Application of unsupervised learning in weight-loss categorisation for weight management programs. in The 10th IEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019. IEEE, IEEE International Conference on Dependable Systems, Services and Technologies, Leeds, United Kingdom, 05/06/2019.

APA

Babajide, O., Hissam, T., Palczewska, A., Astrup, A., Martinez, J. A., Oppert, J. M., & Sørensen, T. I. A. (2019). Application of unsupervised learning in weight-loss categorisation for weight management programs. In The 10th IEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019 IEEE.

Vancouver

Babajide O, Hissam T, Palczewska A, Astrup A, Martinez JA, Oppert JM et al. Application of unsupervised learning in weight-loss categorisation for weight management programs. In The 10th IEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019. IEEE. 2019

Author

Babajide, Oladapo ; Hissam, Tawfik ; Palczewska, Anna ; Astrup, Arne ; Martinez, J Alfredo ; Oppert, Jean Michel ; Sørensen, Thorkild I.A. / Application of unsupervised learning in weight-loss categorisation for weight management programs. The 10th IEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019. IEEE, 2019.

Bibtex

@inproceedings{a6575807cc2341f4a1e94cbb15f400f7,
title = "Application of unsupervised learning in weight-loss categorisation for weight management programs",
abstract = "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 variouscardiovascular 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 whichare required for supervised learning.",
keywords = "The Faculty of Science, Weight management, Weight loss categorisation, Unsupervised learning, Data clustering, Smart health management",
author = "Oladapo Babajide and Tawfik Hissam and Anna Palczewska and Arne Astrup and Martinez, {J Alfredo} and Oppert, {Jean Michel} and S{\o}rensen, {Thorkild I.A.}",
note = "CURIS 2019 NEXS 210",
year = "2019",
month = "6",
day = "19",
language = "English",
isbn = "978-1-7281-1733-1",
booktitle = "The 10th IEEE International Conference on Dependable Systems, Services and Technologies",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Application of unsupervised learning in weight-loss categorisation for weight management programs

AU - Babajide, Oladapo

AU - Hissam, Tawfik

AU - Palczewska, Anna

AU - Astrup, Arne

AU - Martinez, J Alfredo

AU - Oppert, Jean Michel

AU - Sørensen, Thorkild I.A.

N1 - CURIS 2019 NEXS 210

PY - 2019/6/19

Y1 - 2019/6/19

N2 - 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 variouscardiovascular 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 whichare required for supervised learning.

AB - 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 variouscardiovascular 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 whichare required for supervised learning.

KW - The Faculty of Science

KW - Weight management

KW - Weight loss categorisation

KW - Unsupervised learning

KW - Data clustering

KW - Smart health management

M3 - Article in proceedings

SN - 978-1-7281-1733-1

BT - The 10th IEEE International Conference on Dependable Systems, Services and Technologies

PB - IEEE

ER -

ID: 222747272