Application of unsupervised learning in weight-loss categorisation for weight management programs
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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: Conference proceedings. IEEE, 2019. p. 94-101 8770032.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
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 - Conference code: 10
PY - 2019
Y1 - 2019
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 - Faculty of Science
KW - Weight management
KW - Weight loss categorisation
KW - Unsupervised learning
KW - Data clustering
KW - Smart health management
U2 - 10.1109/DESSERT.2019.8770032
DO - 10.1109/DESSERT.2019.8770032
M3 - Article in proceedings
SP - 94
EP - 101
BT - The 10th IEEE International Conference on Dependable Systems, Services and Technologies. DESSERT'2019
PB - IEEE
T2 - IEEE International Conference on Dependable Systems, Services and Technologies
Y2 - 5 June 2019 through 7 June 2019
ER -
ID: 222747272