A machine learning approach to short-term body weight prediction in a dietary intervention program
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Weight and obesity management is one of the emerging challenges in current health management. Nutrient-gene interactions in human obesity (NUGENOB) seek to find various solutions to challenges posed by obesity and over-weight. This research was based on utilising a dietary intervention method as a means of addressing the problem of managing obesity and overweight. The dietary intervention program was done for a period of ten weeks. Traditional statistical techniques have been utilised in analyzing the potential gains in weight and diet intervention programs. This work investigates the applicability of machine learning to improve on the prediction of body weight in a dietary intervention program. Models that were utilised include Dynamic model, Machine Learning models (Linear regression, Support vector machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN)). The performance of these estimation models was compared based on evaluation metrics like RMSE, MAE and R2. The results indicate that the Machine learning models (ANN and RF) perform better than the other models in predicting body weight at the end of the dietary intervention program.
Original language | English |
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Title of host publication | Computational Science - ICCS 2020 : 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020. Proceedings, Part IV |
Editors | V V Krzhizhanovskaya, G Zavodszky, M H Lees, P M A Sloot, J J Dongarra, S Brissos, J Teixeira |
Number of pages | 15 |
Publisher | Springer |
Publication date | 2020 |
Pages | 441-455 |
ISBN (Electronic) | 9783030504229 |
DOIs | |
Publication status | Published - 2020 |
Event | 20th International Conference on Computational Science, ICCS 2020 - Amsterdam, Netherlands Duration: 3 Jun 2020 → 5 Jun 2020 |
Conference
Conference | 20th International Conference on Computational Science, ICCS 2020 |
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Land | Netherlands |
By | Amsterdam |
Periode | 03/06/2020 → 05/06/2020 |
Series | Lecture Notes in Computer Science |
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Volume | 12140 |
ISSN | 0302-9743 |
- Body weight and weight-loss prediction, Supervised machine learning, Weight and obesity management
Research areas
Links
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303700/pdf/978-3-030-50423-6_Chapter_33.pdf
Final published version
ID: 245417680