A Machine Learning Approach to Short-Term Body Weight Prediction in a Dietary Intervention Program Oladapo Babajide 1(&) , Tawfik Hissam 1 , Palczewska Anna 1 , Gorbenko Anatoliy 1 , Arne Astrup 2 , J. Alfredo Martinez 3 , Jean-Michel Oppert 4 , and Thorkild I. A. Sørensen 5 1 School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, UK O.Babajide5449@student.leedsbeckett.ac.uk 2 Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark ast@nexs.ku.dk 3 Centre for Nutrition Research, University of Navarra, CIBERobn Obesity, and IMDEA Program on Precision Nutrition, Madrid, Spain jalfmtz@unav.es 4 Department of Nutrition Pitie-Salpetriere Hospital, Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne University, Paris, France jean-michel.oppert@aphp.fr 5 Novo Nordisk Foundation Center for Basic Metabolic Research and Department of Public Health, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark tias@sund.ku.dk 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: Weight and obesity management  Body weight and weight-loss prediction  Supervised machine learning © Springer Nature Switzerland AG 2020 V. V. Krzhizhanovskaya et al. (Eds.): ICCS 2020, LNCS 12140, pp. 441–455, 2020. https://doi.org/10.1007/978-3-030-50423-6_33