Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DESSERT.2019.8770032 | 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT) |
Keywords | Field | DocType |
Weight Management,weight loss categorisation,Unsupervised Learning,Data clustering,Smart health management. | Computer science,Weight management,Supervised learning,Unsupervised learning,Artificial intelligence,Weight loss,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-1734-8 | 0 | 0.34 |
References | Authors | |
2 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oladapo Babajide | 1 | 0 | 0.34 |
| Hissam Tawfik | 2 | 319 | 43.29 |
| Anna Palczewska | 3 | 0 | 0.34 |
| Anatoliy Gorbenko | 4 | 0 | 0.34 |
| Arne Astrup | 5 | 0 | 0.68 |
| Martinez, J.A. | 6 | 0 | 1.01 |
| Jean-Michel Oppert | 7 | 0 | 0.34 |
| Thomas Sicheritz-Pontén | 8 | 1 | 0.73 |