Obesity prediction using machine learning techniques



  • Fati Musa Federal University Lokoja
  • Dr. Federal University Lokoja
  • Professor Federal University Lokoja


Currently, safeguarding the community is vital in terms of finding solution to health related problems which can be achieved through medical research using the advent of technology. Obesity has become worldwide health concern as it is becoming a threat to the future. It is the most common health problems all over the world. Thousands of diseases as well as risks and death are associated to it. An early prediction of a disease will help both doctors and patients to act and minimize if not total eradication of the root cause or work on preventing the disease symptom from further deterioration. Going through patient’s medical history is one of the methods of identifying a disease which most time consuming as processing manually and it comes with an error-prone analyses and expense. Therefore, there is need to scientifically develop a predicting model of the occurrence of the disease or its existence using an automated technique as it is becoming a need of the day. In this research work, we used machine learning techniques on a public clinical available dataset to predict obesity status using different machine learning algorithms. Five machine learning algorithms were applied. Gboost Classifier, Random Forest Classifier, Decision Tree Classifier, K-Nearest Neighbor and Support Vector Machine and the model has shown promising results with as Gboost classifier achieves the highest accuracy of 99.05% as compared to other classifiers. Meanwhile, the K-Nearest Neighbor gave the relatively strong accuracy of 95.74%.


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Author Biographies

Dr., Federal University Lokoja

Department of Computer science, Lecturer.

Professor, Federal University Lokoja

Department of Computer science, head of ICT


Eduado, D., Fabio, E., & Mendoza, P. (2019). Obesity Level Estimation Software based on Decision Tree. Jornal of Computer Science, 67-77.

WHO. (2021, June 23). Obesity Related Diseases. Retrieved from World Health Organisation: www.who.int

Guterrez, H. M. (2010). Diez problems de la poblacion de jalisco. Una perspective sociodeografica, 25-30.

Hernandez, J. (2011). Obesity and its causes. International Journal for Medical Image.

Del Cisne, P., & Zhingre, O. (2015). Factors that Influence Obesity. 10-13.

Xiaolu, C., Shuo-yu, L., Jin, L., Shiyong , L., Jun, Z., Peng, N., et al. (2021). Does Physical Activity Predict Obesity-A Machine Learning and Statistical Method-Based Analysis. International Journal of environmental research and public Health.

DeGregory, K. W., Patrick , K., DeSilvio, T., & Pleuss, J. D. (2018). A review of machine learning in obesity: machine learning in obesity reaserch. ResearchGate.[8] Zachary, J., Ward, M. P., Sara, N. B., Angie, L., Cradock, Jessical, L., et al. (2019). Projected US State Level Prevalence of Adult Obesity and Severe Obesity. The New England Journal of Medicine, 2440-2450.

Hagai, R., Smadar, S., Shiri, B.-H., Becca, F., Aron, W., & Eran, S. (2021). Prediction of Childhood Obesity from Nationwide Health Records. The Journal of Pediatrics, 132-140.[10] Xuegin, P., Christopher, B. F., Felice, L.-S., & Aaron, J. M. (2021). Prediction of Early Childhood Obesity with Machine Learning and Electronic Health Record Data. International Journal of Medical Informatics.[11] Davila, P., DeGuzman, C. M., Johnson, K., & Serban. (2015). Estimating prevalence of overweight or obese children and adolescents in small geographic areas uning publicly available data. IEEE.

Manna , S., & Jewkes, A. M. (2014). Understanding early childhood obesity risks: An empirical study using fuzzy signatures. IEEE international confluence (pp. 1333-1339). Beijing China : Xplore press.

Adnan, M. H. (2011). A framework for childhood obesity classifications and predictions using NBtree. Proceedings of the 7th International Conference on Information Technology in Asia (pp. 1-6). Kuching, Sarawak, Malaysia: IEEE Xplore Press.

Adnan, M. H., Damanhoori, F. D., & Husain, W. (2010). A survey on the usefulness of data mining for childhood obesity prediction. Proceedings of the 8th AsiaPacific Symposium on Information and Telecommunication Technologies (pp. 1-6). Kuching, Malaysia: IEEE Xplore Press.

Abdullah, F. S., Manan, N. S., Ahmad, A., Wafa, S. W., & Shahril, M. R. (2016). Data miningtechniques for classification of childhood obecity among year 6 school children. proceeding of the international conference on soft computing and data mining (pp. 465-474). Springer: IEEE Xplore press.[16] Tamara, M. D., Mukhopadhyay, S., Aaron , C., & Stephen, M. D. (2015). Machine lerning techniques for prediction on early childhood obesity. Researchgate.

Kapil, J., Niyati, B., & Prashant, S. R. (2018). Obesity prediction using ensemble machine learning approaches . Researchgate.

Brownlee, J. (2020). Ensemble Learning. Machine Learning Mastery.

Donges, N. (2021). A Compete Guide to the Random Forest Algorithm. Expert Contributor Network.

www.geeksforgeeks.org. (2022). www.geeksforgeeks.org. Retrieved from www.geeksforgeeks.org.

Joby, A. (2021, July 19). learn.g2.com. Retrieved from G2 Learn Hub.

Dwivedi, R. (2021, January 29). Machine Learning. United States Artificial Intelligence Institute.



How to Cite

Musa, F., Basaky, F., & E.O, O. (2022). Obesity prediction using machine learning techniques. Journal of Applied Artificial Intelligence, 3(1), 24–33. https://doi.org/10.48185/jaai.v3i1.470