Version 1
: Received: 6 May 2021 / Approved: 10 May 2021 / Online: 10 May 2021 (11:22:56 CEST)
How to cite:
Bandyopadhyay, S.; Dutta, S.; Pramanik, S. Prediction of Weight Gain during COVID-19 for Avoiding Complication in Health. Preprints2021, 2021050177. https://doi.org/10.20944/preprints202105.0177.v1.
Bandyopadhyay, S.; Dutta, S.; Pramanik, S. Prediction of Weight Gain during COVID-19 for Avoiding Complication in Health. Preprints 2021, 2021050177. https://doi.org/10.20944/preprints202105.0177.v1.
Cite as:
Bandyopadhyay, S.; Dutta, S.; Pramanik, S. Prediction of Weight Gain during COVID-19 for Avoiding Complication in Health. Preprints2021, 2021050177. https://doi.org/10.20944/preprints202105.0177.v1.
Bandyopadhyay, S.; Dutta, S.; Pramanik, S. Prediction of Weight Gain during COVID-19 for Avoiding Complication in Health. Preprints 2021, 2021050177. https://doi.org/10.20944/preprints202105.0177.v1.
Abstract
Obesity and overweight is a foremost concern around the globe for each group of age. This can be accelerated by the current imposed lockdown. However, excessive weight gain may result in other chronic diseases. This study has been considering the age group of 25 to 55 years as the sample populations and monitoring them from July, 2020 to November, 2020. The lifestyle of this population, food habit, mental health conditions are explored using deep learning based framework. All these parameters need to be monitored as these have close relation with currently imposed constraints due to COVID-19. A predictive model is constructed using deep learning techniques to predict the risk of gaining weight. The predictive model hybridizes the convolutional layer and gated recurrent neural networks as a unified entity for achieving the objective of early weight gain prediction. The result obtained by this model exhibits an encouraging predictive efficiency of 93.7%.
Keywords
Overweight, obesity, deep learning, Convolutional layer, GRU, COVID-19, lifestyle.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
