Obesity level prediction using deep learning approach – A comparative analysis

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Published: Jul 25, 2024
Keywords:
Obesity level SMOTE TabNet Classifier XGB Classifier GB Classifier

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Srinivasa Gupta Nagarajan
Department of Manufacturing Engineering, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Valarmathi Balasubramanian
Department of Software and Systems Engineering, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Phani Gonugunta
Department of Software and Systems Engineering, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Saran Kumar Gudla
Department of Software and Systems Engineering, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract

Obesity, the excessive accumulation of body fat, affected millions globally and was influenced by eating habits, lack of activity, genetics, environmental factors, and emotional strain. It could lead to severe health issues, including insulin resistance, cardiovascular diseases, cancer, sleep apnea, joint problems, and mental health disorders. This study aimed to predict obesity levels using Machine Learning (ML) and Deep Learning (DL) models on a real-life dataset of obesity patients. The dataset comprised several patient health records with 17 different elements related to obesity, classifying obesity levels into seven types. The study evaluated the accuracy of various models before and after applying the Synthetic Minority Over-sampling Technique (SMOTE). Before SMOTE, the TabNet (T) and XG-Boost (XGB) classifiers achieved high accuracies of 96.6% and 96.2%, respectively, outperforming Random Forest (RF) (94.8%), Multi-Layer Perceptron (MLP) (94.5%), Bagging (B) (94.07%), Decision Tree (DT) (93.6%), Support Vector Machine (SVM) (82.5%), K-Nearest Neighbor (KNN) (75.9%), Stochastic Gradient Descent (SGD) (68.6%), AdaBoost (AB) (28.4%), Stacking (S) (16.8%), and G-Boost (GB) (95.5%). After applying SMOTE, GB and XGB showed improved accuracies of 99.3% and 99%, respectively, surpassing RF (97.4%), Bagging (96.28%), DT (96.9%), SVM (90.3%), KNN (85.7%), SGD (67.6%), AB (34.9%), and Stacking (12.3%). Comparatively, the existing methods showed accuracies with GB (97.2%), DT (96.7%), RF (94.8%), SVM (43.4%), and AB (33.1%), while the proposed models exhibited superior performance: GB (99.3%), DT (96.9%), RF (97.4%), SVM (90.2%), AB (34.9%), XGB (99%), TabNet (98.4%), and MLP (97.7%). The proposed models significantly outperformed the existing ones, demonstrating their effectiveness in predicting obesity levels.

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How to Cite
Nagarajan, S. G. ., Balasubramanian, V., Gonugunta, P. ., & Gudla, S. K. (2024). Obesity level prediction using deep learning approach – A comparative analysis. Engineering and Applied Science Research, 51(4), 540–554. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/255319
Issue
Vol. 51 No. 4 (2024)
Section
ORIGINAL RESEARCH
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Kivrak M. Deep learning-based prediction of obesity levels according to eating habits and physical condition. J Cogn Syst. 2021; 6(1):24-7.

Maria AS, Sunder R, Kumar RS. Obesity risk prediction using machine learning approach. 2023 International Conference on Networking and Communications (ICNWC); 2023 Apr 5-6; Chennai, India. USA: IEEE; 2023. p. 1-7.

Anisat MF, Basaky FD, Osaghae EO. Obesity prediction model using machine learning techniques. J Appl Artif Intell. 2022;3(1):24-33.

Alsareii SA, Awais M, Alamri AM, AlAsmari MY, Irfan M, Raza M, et al. Machine-Learning-Enabled obesity level prediction through electronic health records. Comput Syst Sci Eng. 2023;46(3):3715-28.

Yagin FH, Gülü M, Gormez Y, Castañeda-Babarro A, Colak C, Greco G, et al. Estimation of obesity levels with a trained neural network approach optimized by the Bayesian technique. Appl Sci. 2023;13(6):3875.

Kaur R, Kumar R, Gupta M. Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial intelligence. Endocrine. 2022;78(3):458-69.

Quiroz JPS. Estimation of obesity levels based on dietary habits and condition physical using computational intelligence. Inform Med Unlocked. 2022;29:100901.

Cui T, Chen Y, Wang J, Deng H, Huang Y. Estimation of obesity levels based on Decision trees. 2021 International Symposium on Artificial Intelligence and its Application on Media (ISAIAM); 2021 May 21-23; Xi'an, China. USA: IEEE; 2021. p. 160-5.

Celik Y, Guney S, Dengiz B. Obesity level estimation based on machine learning methods and artificial neural networks. 2021 44th International Conference on Telecommunications and Signal Processing (TSP); 2021 Jul 26-28; Brno, Czech Republic. USA: IEEE; 2021. p. 329-32.

Ferdowsy F, Rahi KSA, Jabiullah MI, Habib MT. A machine learning approach for obesity risk prediction. Curr Res Behav Sci. 2021;2:100053.

Jeon J, Lee S, Oh C. Age-Specific risk factors for the prediction of obesity using a machine learning approach. Front Public Health. 2023;10:1-12.

Molina D, De-La-Hoz A, Mendoza F. Classification and features selection method for obesity level prediction. J Theor Appl Inf Technol. 2021;99(11):2525-36.

Rodríguez E, Rodríguez E, Nascimento L, da Silva A, Marins F. Machine learning techniques to predict overweight or obesity. IDDM-2021: 4th International Conference on Informatics & Data-Driven Medicine; 2021 Nov 19-21; Valencia, Spain. p. 190-204.

Pinto KA, Abdullah NL, Keikhosrokiani P. Diet & exercise classification using machine learning to predict obese patient’s weight loss. 2021 International Congress of Advanced Technology and Engineering (ICOTEN); 2021 Jul 4-5; Taiz, Yemen. USA: IEEE; 2021. p. 1-5.

Cervantes RC, Palacio UM. Estimation of obesity levels based on computational intelligence. Informa Med Unlocked. 2020;21: 100472.

Deniz S, Şirin H, Kıvrak M, Kaplan Z, Ketrez G, Üner S. Factors associated with overweight and obesity in students of 5-14 age group in Mersin. Gulhane Med J. 2020;62(4):245-53.

De-La-Hoz-Correa E, Mendoza-Palechor FE, De-La-Hoz-Manotas A, Morales Ortega RC, Beatriz Adriana SH. Obesity level estimation software based on Decision trees. J Comput Sci. 2019;15(1):66-77.

Palechor FM, de la Hoz Manotas A. Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru, and Mexico. Data Brief. 2019;25:104344.

DeGregory KW, Kuiper P, DeSilvio T, Pleuss JD, Miller R, Roginski JW, et al. A review of machine learning in obesity. Obes Rev. 2018;19(5):668-85.

Montañez CAC, Fergus P, Hussain A, Al-Jumeily D, Abdulaimma B, Hind J, et al. Machine learning approaches for the prediction of obesity using publicly available genetic profiles. 2017 International Joint Conference on Neural Networks (IJCNN); 2017 May 14-19; Anchorage, USA. USA: IEEE; 2017. p. 2743-50.

Yahia N, Wang D, Rapley M, Dey R. Assessment of weight status, dietary habits and beliefs, physical activity, and nutritional knowledge among university students. Perspect Public health. 2016;136(4):231-44.

Dugan TM, Mukhopadhyay S, Carroll A, Downs S. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform. 2015;6(3):506-20.

Davila-Payan C, DeGuzman M, Johnson K, Serban N, Swann J. Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data. Prev Chronic Dis. 2015;12:1-13.

Muhamad Adnan MHB, Husain W, Abdul Rashid N. A hybrid approach using Naïve Bayes and Genetic Algorithm for childhood obesity prediction. 2012 International Conference on Computer & Information Science (ICCIS); 2012 Jun 12-14; Kuala Lumpur, Malaysia. USA: IEEE; 2012. p. 281-5.

Muhamad Adnan MHB, Husain W, Damanhoori F. A survey on utilization of data mining for childhood obesity prediction. 8th Asia-Pacific Symposium on Information and Telecommunication Technologies; 2010 Jun 15-18; Kuching, Malaysia. USA: IEEE; 2010. p. 1-6.

Calderón-Díaz M, Serey-Castillo LJ, Vallejos-Cuevas EA, Espinoza A, Salas R, Macías-Jiménez MA. Detection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques. Procedia Comput Sci. 2023;220:978-83.

Pereira NC, D'souza J, Rana P, Solaskar S. Obesity-related disease prediction from healthcare communities using machine learning. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT); 2019 Jul 6-8; Kanpur, India. USA: IEEE; 2019. p. 1-7.

Nicklas TA, Yang SJ, Baranowski T, Zakeri I, Berenson G. Eating patterns and obesity in children. The Bogalusa Heart Study. Am J Prev Med. 2003;25(1):9-16.

Zhao R, Zhao L, Gao X, Yang F, Yang Y, Fang H, Ju L, et al. Geographic variations in dietary patterns and their associations with overweight/obesity and hypertension in China: findings from China nutrition and health surveillance (2015–2017). Nutrients. 2022;14(19):3949.

Alkhalaf M, Yu P, Shen J, Deng C. A review of the application of machine learning in adult obesity studies. Appl Comput Intell. 2022;2(1):32-48.

Devi KN, Krishnamoorthy N, Jayanthi P, Karthi S, Karthik T, Kiranbharath K. Machine learning based adult obesity prediction. 2022 International Conference on Computer Communication and Informatics (ICCCI); 2022 Jan 25-27; Coimbatore, India. USA: IEEE; 2002. p. 1-5.