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A machine learning approach to short-term body weight prediction in a dietary intervention program. / Babajide, Oladapo; Hissam, Tawfik; Anna, Palczewska; Anatoliy, Gorbenko; Astrup, Arne; Alfredo Martinez, J; Oppert, Jean-Michel; Sørensen, Thorkild I A.
Computational Science - ICCS 2020: 20
th International Conference, Amsterdam, The Netherlands, June 3-5, 2020. Proceedings, Part IV. red. / V V Krzhizhanovskaya; G Zavodszky; M H Lees; P M A Sloot; J J Dongarra; S Brissos; J Teixeira. Springer, 2020. s. 441-455 (Lecture Notes in Computer Science, Bind 12140).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Babajide, O, Hissam, T, Anna, P, Anatoliy, G, Astrup, A, Alfredo Martinez, J, Oppert, J-M
& Sørensen, TIA 2020,
A machine learning approach to short-term body weight prediction in a dietary intervention program. i VV Krzhizhanovskaya, G Zavodszky, MH Lees, PMA Sloot, JJ Dongarra, S Brissos & J Teixeira (red),
Computational Science - ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020. Proceedings, Part IV. Springer, Lecture Notes in Computer Science, bind 12140, s. 441-455, 20th International Conference on Computational Science, ICCS 2020, Amsterdam, Holland,
03/06/2020.
https://doi.org/10.1007/978-3-030-50423-6_33APA
Babajide, O., Hissam, T., Anna, P., Anatoliy, G., Astrup, A., Alfredo Martinez, J., Oppert, J-M.
, & Sørensen, T. I. A. (2020).
A machine learning approach to short-term body weight prediction in a dietary intervention program. I V. V. Krzhizhanovskaya, G. Zavodszky, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, S. Brissos, & J. Teixeira (red.),
Computational Science - ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020. Proceedings, Part IV (s. 441-455). Springer. Lecture Notes in Computer Science Bind 12140
https://doi.org/10.1007/978-3-030-50423-6_33Vancouver
Babajide O, Hissam T, Anna P, Anatoliy G, Astrup A, Alfredo Martinez J o.a.
A machine learning approach to short-term body weight prediction in a dietary intervention program. I Krzhizhanovskaya VV, Zavodszky G, Lees MH, Sloot PMA, Dongarra JJ, Brissos S, Teixeira J, red., Computational Science - ICCS 2020: 20
th International Conference, Amsterdam, The Netherlands, June 3-5, 2020. Proceedings, Part IV. Springer. 2020. s. 441-455. (Lecture Notes in Computer Science, Bind 12140).
https://doi.org/10.1007/978-3-030-50423-6_33Author
Babajide, Oladapo ; Hissam, Tawfik ; Anna, Palczewska ; Anatoliy, Gorbenko ; Astrup, Arne ; Alfredo Martinez, J ; Oppert, Jean-Michel ; Sørensen, Thorkild I A. / A machine learning approach to short-term body weight prediction in a dietary intervention program. Computational Science - ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020. Proceedings, Part IV. red. / V V Krzhizhanovskaya ; G Zavodszky ; M H Lees ; P M A Sloot ; J J Dongarra ; S Brissos ; J Teixeira. Springer, 2020. s. 441-455 (Lecture Notes in Computer Science, Bind 12140).
Bibtex
@inproceedings{0b1195411ad84a1c881fe3504ecf839e,
title = "A machine learning approach to short-term body weight prediction in a dietary intervention program",
abstract = "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.",
keywords = "Body weight and weight-loss prediction, Supervised machine learning, Weight and obesity management",
author = "Oladapo Babajide and Tawfik Hissam and Palczewska Anna and Gorbenko Anatoliy and Arne Astrup and {Alfredo Martinez}, J and Jean-Michel Oppert and S{\o}rensen, {Thorkild I A}",
note = "CURIS 2020 NEXS 243; 20th International Conference on Computational Science, ICCS 2020 ; Conference date: 03-06-2020 Through 05-06-2020",
year = "2020",
doi = "10.1007/978-3-030-50423-6_33",
language = "English",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "441--455",
editor = "Krzhizhanovskaya, {V V} and G Zavodszky and Lees, {M H} and Sloot, {P M A} and Dongarra, {J J} and S Brissos and J Teixeira",
booktitle = "Computational Science - ICCS 2020",
address = "Switzerland",
}
RIS
TY - GEN
T1 - A machine learning approach to short-term body weight prediction in a dietary intervention program
AU - Babajide, Oladapo
AU - Hissam, Tawfik
AU - Anna, Palczewska
AU - Anatoliy, Gorbenko
AU - Astrup, Arne
AU - Alfredo Martinez, J
AU - Oppert, Jean-Michel
AU - Sørensen, Thorkild I A
N1 - CURIS 2020 NEXS 243
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Body weight and weight-loss prediction
KW - Supervised machine learning
KW - Weight and obesity management
U2 - 10.1007/978-3-030-50423-6_33
DO - 10.1007/978-3-030-50423-6_33
M3 - Article in proceedings
AN - SCOPUS:85087283932
T3 - Lecture Notes in Computer Science
SP - 441
EP - 455
BT - Computational Science - ICCS 2020
A2 - Krzhizhanovskaya, V V
A2 - Zavodszky, G
A2 - Lees, M H
A2 - Sloot, P M A
A2 - Dongarra, J J
A2 - Brissos, S
A2 - Teixeira, J
PB - Springer
T2 - 20th International Conference on Computational Science, ICCS 2020
Y2 - 3 June 2020 through 5 June 2020
ER -
