Kişilerin Sosyal ve Fiziksel Aktivitelerine Göre Obezite Durumunun Analizi için Yapay Zeka Tekniklerinin Kullanımı

Year 2024, Volume: 9 Issue: 1, 217 - 239, 29.06.2024

Nigmet Koklu , Süleyman Alpaslan Sulak

https://doi.org/10.33484/sinopfbd.1445215

Abstract

Obezite, genetik ve çevresel etkileşimlere sahip ciddi ve kronik bir hastalıktır. Sağlığa zararlı olan vücuttaki aşırı miktardaki yağ dokusu olarak tanımlanır. Obezitenin başlıca risk faktörleri, sosyal, psikolojik ve beslenme alışkanlıklarını içerir. Obezite, dünya genelinde tüm yaş grupları için önemli bir sağlık sorunudur. Şu anda dünya genelinde 2 milyardan fazla insan obez veya aşırı kilolu durumdadır. Araştırmalar, obezitenin önlenebileceğini göstermektedir. Bu çalışmada, obezite riski taşıyan bireyleri tanımlamak için yapay zeka yöntemleri kullanıldı. Obezite veri setini oluşturmak için 1610 birey üzerinde çevrimiçi bir anket yapıldı. Anket verilerini analiz etmek için literatürde yaygın olarak kullanılan dört yapay zeka yöntemi olan Yapay Sinir Ağı, K En Yakın Komşu, Rastgele Orman ve Destek Vektör Makinesi, kullanıldı. Bu analizin sonucunda, obezite sınıfları sırasıyla %74.96, %74.03, %74.03 ve %87.82 başarı oranlarıyla doğru bir şekilde tahmin edildi. Rastgele Orman, bu veri seti için en başarılı yapay zeka yöntemi oldu ve obeziteyi %87.82 başarı oranıyla doğru bir şekilde sınıflandırdı.

Keywords

Obezite veri seti, yapay zeka yöntemleri, yapay sinir ağı, destek vektör makinesi, k- en yakın komşu, rastgele orman

Using Artificial Intelligence Techniques for the Analysis of Obesity Status According to the Individuals' Social and Physical Activities

Abstract

Obesity is a serious and chronic disease with genetic and environmental interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. Currently, more than 2 billion people worldwide are obese or overweight. Research has shown that obesity can be prevented. In this study, artificial intelligence methods were used to identify individuals at risk of obesity. An online survey was conducted on 1610 individuals to create the obesity dataset. To analyze the survey data, four commonly used artificial intelligence methods in literature, namely Artificial Neural Network, K Nearest Neighbors, Random Forest and Support Vector Machine, were employed after pre-processing. As a result of this analysis, obesity classes were predicted correctly with success rates of 74.96%, 74.03%, 74.03% and 87.82%, respectively. Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%.

Keywords

Obesity dataset, artificial intelligence methods, artificial neural network, support vector machine, k nearest neighbors, random forest

Ethical Statement

The ethics committee document of the research was received with decision number 2023/201 at the meeting numbered 06 of Necmettin Erbakan University Social and Human Sciences Scientific Research Ethics Committee dated 12/05/2023

References

• Yetkin F. (2008). Konya il merkezinde özel hastanelere başvuran 18-60 yaş grubu kadınların obezite prevalansı and bunu etkileyen etmenler üzerine bir araştırma. Yayınlanmamış [Yüksek Lisans Tezi]. Konya. s. 66.
• Lakdawalla D & Philipson T. (2009). The growth of obesity and technological change. Economics & Human Biology, 7:283-293. https://doi.org/10.1016/j.ehb.2009.08.001
• Tan, K. C. B. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The lancet. http://dx.doi.org/10.1016/S0140-6736(03)15268-3
• Cervantes, R. C & Palacio, U. M. (2020). Estimation of obesity levels based on computational intelligence. Informatics in Medicine Unlocked, 21, 100472. https://doi.org/10.1016/j.imu.2020.100472
• Hill, J. O., Wyatt, H. R & Peters, J. C. (2012). Energy balance and obesity. Circulation, 126, 126-132. https://doi.org/10.1161/CIRCULATIONAHA.111.087213
• Kopelman, P. G. (2000). Obesity as a medical problem. Nature, 404, 635-643. https://doi.org/10.1038/35007508
• Deckelbaum, R. J., & Williams, C. L. (2001). Childhood obesity: the health issue. Obesity Research. 9, 239-243. https://doi.org/10.1038/oby.2001.125
• Turan, T. (2024). Optimize edilmiş denetimli öğrenme algoritmaları ile obezite analizi ve tahmini. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 14(2), 301-312. https://doi.org/10.29048/makufebed.1372323
• Vizmanos, B., Cascales, A. I., Rodríguez‐Martín, M., Salme-rón, D., Morales, E., Aragón‐Alonso, A., Garaulet, M. (2023). Lifestyle mediators of associations among siestas, obesity, and metabolic health. Obesity, 31(5): 1227-1239. https://doi.org/10.1002/oby.23765
• Ogden, C. L., Carroll, M. D., Curtin, L. R., McDowell, M. A., Tabak, C. J., & Flegal, K. M. (2006). Prevalence of overweight and obesity in the United States, 1999-2004. Jama, 295(13), 1549-1555. https://doi.org/10.1001/jama.295.13.1549
• Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., ... & Gakidou, E. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 384(9945), 766-781. https://doi.org/10.1016/S0140-6736(14)60460-8
• Dinsa, G. D, Goryakin, Y., Fumagalli, E., & Suhrcke, M. (2012). Obesity and socioeconomic status in developing countries: a systematic review. Obesity Reviews, 13, 1067-1079. https://doi.org/10.1111/j.1467-789X.2012.01017.x
• Stavridou, A., Kapsali, E., Panagouli, E., Thirios, A., Polychronis, K., Bacopoulou, F., Psaltopoulou, T., Tsolia, M., Sergentanis, T. N., & Tsitsika, A. (2021). Obesity in children and adolescents during COVID-19 pandemic. Children, 8(2), 135. https://doi.org/10.3390/children8020135
• Ryan, D., Barquera, S., Barata Cavalcanti, O., & Ralston, J. (2021). The global pandemic of overweight and obesity: Addressing a twenty-First century multifactorial disease. In Handbook of global health (pp. 739-773). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-45009-0_39
• Fock, K. M., & Khoo, J. (2013). Diet and exercise in management of obesity and overweight. Journal of Gastroenterology and Hepatology, 28, 59-63. https://doi.org/10.1111/jgh.12407
• The GBD 2015 Obesity Collaborators. (2017). Health effects of overweight and obesity in 195 countries over 25 years. New England Journal of Medicine, 377(1), 13-27. https://doi.org/10.1056/NEJMoa1614362
• Hainerová, I. A., & Lebl, J. (2013). Treatment options for children with monogenic forms of obesity. Nutrition and Growth, 106, 105-112. https://doi.org/10.1159/000342556
• Reilly, J. J., Armstrong, J., Dorosty, A. R., Emmett, P. M., Ness, A., Rogers, I., Steer, C., Sherriff, A. & Avon Longitudinal Study of Parents and Children Study Team (2005). Early life risk factors for obesity in childhood: cohort study. The BMJ, 330(7504), 1357. https://doi.org/10.1136/bmj.38470.670903.E0
• Lopez, R. P. (2007). Neighborhood risk factors for obesity. Obesity, 15(8), 2111-2119. https://doi.org/10.1038/oby.2007.251
• Komurcu, A. & Derin, D. O. (2024). Sosyal medya kullanımının beden algısı ve yeme tutumuna etkisi. Beslenme Bilimleri Alanında Uluslararası Araştırmalar I, 57.
• Yazıcı-Gulay, M., Korkmaz, Z., Erten, Z. K., & Gürbüz, K. (2021). Çocukların fiziksel aktivite, obezite düzeylerinin incelenmesi: Kayseri ili örneği. Genel Sağlık Bilimleri Dergisi, 3(3), 228-238. https://doi.org/10.51123/jgehes.2021.32
• Prentice, A. M., Black, A. E., Coward, W. A., & Cole, T. J. (1996). Energy expenditure in overweight and obese adults in affluent societies: an analysis of 319 doubly-labelled water measurements. European Journal of Clinical Nutrition, 50(2), 93-97.
• Finucane, M. M, Stevens, G. A., Cowan, M. J, Danaei, G., Lin, J. K., Paciorek, C. J., Singh, G. M., Gutierrez, H. R., Lu, Y., & Bahalim, A. N. (2011). National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9· 1 million participants. The Lancet, 377, 557-567. https://doi.org/10.1016/S0140-6736(10)62037-5
• Reinehr, T. (2010). Obesity and thyroid function. Molecular and Cellular Endocrinology, 316, 165-171. https://doi.org/10.1016/j.mce.2009.06.005
• Friedman, K. E, Reichmann, S. K., Costanzo, P. R & Musante, G. J. (2002). Body image partially mediates the relationship between obesity and psychological distress. Obesity Research, 10, 33-41. https://doi.org/10.1038/oby.2002.5
• Bakhshi, E., Eshraghian, M. R., Mohammad, K., Foroushani, A. R., Zeraati, H., Fotouhi, A., Siassi, F., & Seifi, B. (2008). Sociodemographic and smoking associated with obesity in adult women in Iran: results from the National Health Survey. Journal of Public Health, 30, 429-435. https://doi.org/10.1093/pubmed/fdn024
• Hills, A. P., Andersen, L. B., & Byrne, N. M. (2011). Physical activity and obesity in children. British Journal of Sports Medicine, 45(11), 866-870. https://doi.org/10.1136/bjsports-2011-090199
• Summerbell, C. D., Waters, E., Edmunds, L., Kelly, S. A., Brown, T., & Campbell, K. J. (2005). Interventions for preventing obesity in children. Cochrane Database of Systematic Reviews, (3). https://doi.org/10.1002/14651858.CD001871.pub2
• Jurić, P., Jurak, G., Morrison, S. A., Starc, G., & Sorić, M. (2023). Effectiveness of a population‐scaled, school‐based physical activity intervention for the prevention of childhood obesity. Obesity, 31(3), 811-822. https://doi.org/10.1002/oby.23695
• Strong, W. B., Malina, R. M., Blimkie, C. J., Daniels, S. R., Dishman, R. K., Gutin, B., Hergenroeder, A. C., Must, A., Nixon, P. A , Pivarnik, J M., Rowland, T., Trost, S., & Trudeau, F. (2005). Evidence based physical activity for school-age youth. The Journal of Pediatrics, 146(6), 732-737. https://doi.org/10.1016/j.jpeds.2005.01.055
• Sember, V., Jurak, G., Kovač, M., Morrison, S. A., & Starc, G. (2020). Children's physical activity, academic performance, and cognitive functioning: a systematic review and meta-analysis. Frontiers in Public Health, 8, 307. https://doi.org/10.3389/fpubh.2020.00307
• Canoy, D., & Buchan, I. (2007). Challenges in obesity epidemiology. Obesity Reviews, 8, 1-11. https://doi.org/10.1111/j.1467-789X.2007.00310.x
• Moreno, L. A., & Rodriguez, G. (2007). Dietary risk factors for development of childhood obesity. Current Opinion in Clinical Nutrition & Metabolic Care, 10(3), 336-341. https://doi.org/10.1097/MCO.0b013e3280a94f59
• Akın, E., & Şahin, M. E. (2024). Derin öğrenme ve yapay sinir ağı modelleri üzerine bir inceleme. EMO Bilimsel Dergi, 14(1), 27-38
• Maharana, A., & Nsoesie, E. O. (2018). Use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity. JAMA Network Open, 1(4), 181535-181535. https://doi.org/10.1001/jamanetworkopen.2018.1535
• Alkhalaf, M., Yu, P., Shen, J., & Deng, C. (2022). A review of the application of machine learning in adult obesity studies. Applied Computing and Intelligence, 2(1), 32-48. https://doi.org/10.3934/aci.2022002
• Uribe, A. L. M., & Patterson, J. (2023). Are nutrition professionals ready for artificial intelligence? Journal of Nutrition Education and Behavior, 55(9), 623. https://doi.org/10.1016/j.jneb.2023.07.007
• Atasoy, Z. B. K., Avcı, E., Beydoğan, R., Ozdemir, E., & Göktaş, P. (2024). Yapay Zeka ve Beslenme. In Göç, Ö. (Ed). Sağlık&Bilim 2023 Yeni Nesil Teknolojiler. Efeakademi Yayınları. https://doi.org/10.59617/efepub202367
• Masethe, H. D & Masethe, M. A. (2014, 22-24 October). Prediction of heart disease using classification algorithms. Proceedings of the world Congress on Engineering and computer Science. San Francisco, USA
• Tekin, N. (2023). Eğitimde yapay zekâ: türkiye kaynaklı araştırmaların eğilimleri üzerine bir içerik analizi. Necmettin Erbakan Üniversitesi Ereğli Eğitim Fakültesi Dergisi, 5(Özel Sayı), 387-411. https://doi.org/10.51119/ereegf.2023.49
• Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018). A systematic review on healthcare analytics: application and theoretical perspective of data mining. Healthcare, 6(2). https://doi.org/10.3390/healthcare6020054
• Lucas P. (2004). Bayesian analysis, pattern analysis, and data mining in health care. Current Opinion in Critical Care, 10, 399-403. https://doi.org/10.1097/01.ccx.0000141546.74590.d6
• Jacob, S. G & Ramani, R. G. (2012). Data mining in clinical data sets: a review. International Journal of Applied Information Systems, 4(6), 15-26.
• Milovic, B., & Milovic, M. (2012). Prediction and decision making in health care using data mining. Kuwait Chapter of the Arabian Journal of Business and Management Review,1(12), 126-136.
• Abdullah, F. S., Manan, N. S. A., Ahmad, A., Wafa, S.W., Shahril, M. R., Zulaily, N., Amin, R.M., & Ahmed, A. (2017). Data mining techniques for classification of childhood obesity among year 6 school children. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-51281-5_47
• Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R., & Ghogho, M. (2018). Deep recurrent neural network for intrusion detection in sdn-based networks. 2018 IEEE International Conference on Network Softwarization (NetSoft 2018)- Technical Sessions. 202-206. https://doi.org/10.1109/NETSOFT.2018.8460090
• Taspinar, Y. S., Cinar, I., & Koklu, M. (2021). Prediction of computer type using benchmark scores of hardware units. Selcuk University Journal of Engineering Sciences, 20, 11-17.
• Vapnik, V. N. (1999). The Nature of Statistical Learning Theory. Springer Science & Business media.
• Dwivedi, A. K. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing and Applications, 29, 685-693. https://doi.org/10.1007/s00521-016-2604-1
• Unal, Y., Taspinar, Y. S., Cinar, I., Kursun, R., & Koklu, M. (2022). Application of pre-trained deep convolutional neural networks for coffee beans species detection. Food Analytical Methods, 15, 3232-3243. https://doi.org/10.1007/s12161-022-02362-8
• Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40-79. https://doi.org/10.1214/09-SS054
• Şeker, A., Diri, B., & Balık, H. H. (2017). Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47-64.
• Keskenler, M. F., & Keskenler, E. F. (2017). Geçmişten günümüze yapay sinir ağları ve tarihçesi. Takvim-I Vekayi, 5(2), 8-18.
• Haykin, S. (2009). Neural Networks and Learning Machines, 3/E: Pearson Education India.
• Tosunoğlu, E., Yılmaz, R., Özeren, E., & Sağlam, Z. (2021). Eğitimde makine öğrenmesi: araştırmalardaki güncel eğilimler üzerine inceleme. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 3(2), 178-199. https://doi.org/10.38151/akef.2021.16
• Ozkan, I. A., Koklu, M & Sert, I. U. (2018). Diagnosis of urinary tract infection based on artificial intelligence methods. Computer Methods and Programs in Biomedicine, 166, 51-59. https://doi.org/10.1016/j.cmpb.2018.10.007
• Kim P. (2017). Matlab Deep Learning. Springer.
• Atman Uslu, N., & Onan, A. (2023) Investigating computational ıdentity and empowerment of the students studying programming: A text mining study. Necmettin Erbakan Üniversitesi Ereğli Eğitim Fakültesi Dergisi, 5(1), 29-45. https://doi.org/10.51119/ereegf.2023.29
• Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2018). A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77(2), 647-664.
• Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). [Internet]. 9:381-386. https://doi.org/10.21275/ART20203995
• Tien Bui D, Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13, 361-378. https://doi.org/10.1007/s10346-015-0557-6
• Jakkula, V. (2006). Jakkula, V. (2006). Tutorial on support vector machine (svm). School of EECS, Washington State University, 37(2.5), 3.
• Patle, A., & Chouhan, D. S. (2013, 23-25 January). SVM kernel functions for classification. International Conference on Advances in Technology and Engineering (ICATE, 2013). Mumbai, India. https://doi.org/10.1109/ICAdTE.2013.6524743
• Yu, H., & Kim, S. (2012). SVM Tutorial-Classification, Regression and Ranking.In Rozenberg, G., Back, T., & Kok, J. N. (Eds), Handbook of Natural Computing, (pp. 479-506). Springer. https://doi.org/10.1007/s10462-018-9614-6
• Chauhan, V. K., Dahiya, K., & Sharma, A. (2019). Problem formulations and solvers in linear SVM: a review. Artificial Intelligence Review, 52(2), 803-855. https://doi.org/10.1007/s10462-018-9614-6
• Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37-66. https://doi.org/10.1007/BF00153759
• Zhang, M. L., & Zhou, Z. H., (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40, 2038-2048. https://doi.org/10.1016/j.patcog.2006.12.019
• Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 1-21. https://doi.org/10.1007/s42979-021-00592-x
• Breiman L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
• Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554. https://doi.org/10.1109/ACCESS.2019.2923707
• Archer, K. J., & Kimes, R. V. (2008). Empirical characterization of random forest variable importance measures. Computational Statistics & Data Analysis, 52, 2249-2260. https://doi.org/10.1016/j.csda.2007.08.015
• Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39:2784-2817. https://doi.org/10.1080/01431161.2018.1433343
• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
• Spackman, K. A. (1989). Signal detection theory: Valuable tools for evaluating inductive learning. Proceedings of the Sixth International Workshop on Machine Learning, 160-163. https://doi.org/10.1016/B978-1-55860-036-2.50047-3
• Pepe, M. S. (1997). A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing. Biometrika, 84, 595-608. https://doi.org/10.1093/biomet/84.3.595
• Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests For Classification and Prediction: Oxford University Press, USA.
• Luckett, D. J., Laber, E. B., El‐Kamary, S.S., Fan, C., Jhaveri, R., Perou, C. M., Shebl, F. M & Kosorok, M. R. (2021). Receiver operating characteristic curves and confidence bands for support vector machines, Biometrics, 77, 1422-1430. https://doi.org/10.1111/biom.13365
• Narkhede, S. (2018). Understanding auc-roc curve. Towards Data Science, 26, 220-227.