Application of unsupervised learning in weight-loss categorisation for weight management programs
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
|Titel||The 10th IEEE International Conference on Dependable Systems, Services and Technologies : DESSERT'2019|
|Status||E-pub ahead of print - 19 jun. 2019|
|Begivenhed||IEEE International Conference on Dependable Systems, Services and Technologies: DESSERT'2019 - Leeds Beckett University, Leeds, Storbritannien|
Varighed: 5 jun. 2019 → 7 jun. 2019
Konferencens nummer: 10
|Konference||IEEE International Conference on Dependable Systems, Services and Technologies|
|Lokation||Leeds Beckett University|
|Periode||05/06/2019 → 07/06/2019|
CURIS 2019 NEXS 210
- Det Naturvidenskabelige Fakultet