Interpretable Models For ML-Based Classification of Obesity

Author:

Khater Tarek1ORCID,Tawfik Hissam1ORCID,Sowdagar Sahar1ORCID,Singh Balbir2ORCID

Affiliation:

1. Department of Electrical Engineering, University of Sharjah, United Arab Emirates

2. School of Built Environment, Engineering and Computing, Leeds Beckett University, United Kingdom

Publisher

ACM

Reference21 articles.

1. B. Singh and H. Tawfik , “ Machine learning approach for the early prediction of the risk of overweight and obesity in young people,” in Computational Science–ICCS 2020 : 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part IV 20 . Springer , 2020 , pp. 523– 535 . B. Singh and H. Tawfik, “Machine learning approach for the early prediction of the risk of overweight and obesity in young people,” in Computational Science–ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part IV 20. Springer, 2020, pp. 523–535.

2. Socio-demographic determinants of obesity indexes in Iran:findings from a nationwide STEPS survey

3. Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements

4. Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions

5. A Machine Learning Approach for Predicting Weight Gain Risks in Young Adults

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