Predicting Binge Eating Disorder Using Machine Learning Methods

Year 2024, , 1129 - 1137, 01.10.2024

Serel Akyol , Ayşegül Bayramoğlu

https://doi.org/10.35414/akufemubid.1451334

Abstract

Eating disorders are enduring conditions characterized by elevated rates of mortality and morbidity, presenting a serious threat to life. Among these disorders, binge eating disorder is the most prevalent. Therefore, it is an important health problem that often results in obesity worldwide. This study was conducted to evaluate the eating attitudes and behaviors of university students and predict binge eating disorder using machine learning methods. The study was carried out on 306 individuals (117 males, 189 females). Individuals' personal characteristics were questioned with the questionnaire form. The Bulimic Investigatory Test Edinburgh (BITE) test was used to determine whether individuals taking part in the study had binge eating disorder. In this study, in which binge eating disorder was classified, different artificial neural network models were created by changing the basic parameters, and the optimum model was assessed accordingly. Among the models created with different layers and activation functions, the optimum results were obtained using the number of fully connected layers as 2, first and second layers' sizes as 10, and ReLU, a non-linear activation function, in the Bilayered Neural Network structure. This study is the first trial in which binge eating disorder is predicted using machine learning methods, and we believe that machine learning is an important tool to help researchers and clinicians diagnose, prevent, and treat eating disorders at an early stage.

Keywords

Machine learning algorithms, Binge eating disorder, Bulimic investigatory test Edinburgh (BITE), Eating disorder

Yeme Bozukluklarının Makine Öğrenmesi Yöntemleri Kullanılarak Tahmin Edilmesi

Abstract

Yeme bozuklukları, yüksek ölüm ve hastalık oranlarıyla karakterize deilen ve yaşam için ciddi bir tehdit oluşturan kalıcı durumlardır. Bunlar arasında en yaygın olanı tıkanırcasına yeme bozukluğudur. Bu nedenle dünya çapında sıklıkla obeziteyle sonuçlanan önemli bir sağlık sorunudur. Bu çalışma, üniversite öğrencilerinin yeme tutum ve davranışlarının değerlendirilmesi ve tıkınırcasına yeme bozukluğunun makine öğrenmesi yöntemleri kullanılarak tahmin edilmesi amacıyla yapılmıştır. Araştırma 306 kişi (117 erkek, 189 kadın) üzerinde gerçekleştirilmiştir. Bireylerin kişisel özellikleri anket formu ile sorgulanmıştır. Çalışmaya katılan bireylerde tıkınırcasına yeme bozukluğu olup olmadığını tespit etmek amacıyla, Bulimic Investigatory Test Edinburgh (BITE) testi kullanılmıştır. Tıkınırcasına yeme bozukluğunun tahmin edildiği bu çalışmada, temel parametreler değiştirilerek farklı yapay sinir ağı modelleri oluşturulmuş ve buna göre optimum model değerlendirilmiştir. Farklı katmanlar ve aktivasyon fonksiyonları ile oluşturulan modeller arasında Çift Katmanlı Sinir Ağında tam bağlantılı katman sayısı 2, birinci ve ikinci katman boyutları 10 ve doğrusal olmayan aktivasyon fonksiyonu olan ReLU kullanılarak optimum sonuçlar elde edilmiştir. Bu çalışma, anket çalışmalarından tıkınırcasına yeme bozukluğunun makine öğrenmesi yöntemleri kullanılarak tahmin edildiği ilk çalışma olup, makine öğreniminin, araştırmacıların ve klinisyenlerin yeme bozukluklarının erken teşhisi, önlenmesi ve tedavisine yardımcı olacak önemli bir araç olduğuna inanıyoruz.

Keywords

Makine öğrenmesi algoritmaları, Aşırı yeme bozukluğu, Bulimic investigatory test Edinburgh (BITE), Yeme bozukluğu

References

• Affonso, C., et al., 2017. Deep Learning for biological image classification. Expert Systems with Applications, 85, 114–22. https://doi.org/10.1016/j.eswa.2017.05.039
• Aggarwal, C.C. and ChengXiang Z., 2012. Mining Text Data. New York, Springer. https://doi.org/10.1007/978-1-4614-3223-4_9
• Albertsen, M.N., Eli, N., and Målfrid, R., 2019. Patients’ Experiences from Basic Body Awareness Therapy in the Treatment of Binge Eating Disorder -Movement toward Health: A Phenomenological Study. Journal of Eating Disorders, 7, 1, 1–12. https://doi.org/10.1186/s40337-019-0264-0
• Alunni, V. et al., 2015. Comparing Discriminant Analysis and Neural Network for The Determination of Sex Using Femur Head Measurements. Forensic Science International, 253, 81–87. https://doi.org/10.1016/j.forsciint.2015.05.023
• American Psychiatric Association, 2013. Diagnostic Diagnostic Ans Statistical Manual of Mental Disorders DSM-5, Fifth Edit, Arlington, VA.
• Ashour, A.S., et al., 2018. Ensemble of Subspace Discriminant Classifiers for Schistosomal Liver Fibrosis Staging in Mice Microscopic Images. Health Information Science and Systems, 6, 1, 1–10. https://doi.org/10.1007/s13755-018-0059-8
• Atalay, M., and Çelik E., 2017. Büyük Veri Anali̇zi̇nde Yapay Zekâ ve Maki̇ne Öğrenmesi̇ Uygulamaları. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9, 22, 155–72. https://doi.org/10.20875/makusobed.309727
• Ayhan, S., and Erdoğmuş, Ş., 2014. Destek Vektör Makineleriyle Sınıflandırma Problemlerinin Çözümü İçin Çekirdek Fonksiyonu Seçimi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 9, 1, 175–201. https://doi.org/10.17153/eoguiibfd.33265
• Badrasawi, M.M., and Zidan, S.J., 2019. Binge Eating Symptoms Prevalence and Relationship with Psychosocial Factors among Female Undergraduate Students at Palestine Polytechnic University: A Cross-Sectional Study. Journal of Eating Disorders, 7, 1, 1–8. https://doi.org/10.1186/s40337-019-0263-1
• Barkana, B.D., Saricicek, I. and Yildirim, B., 2017. Performance Analysis of Descriptive Statistical Features in Retinal Vessel Segmentation via Fuzzy Logic , ANN , SVM , and Classifier Fusion. Knowledge-Based Systems, 118, 165–76. https://doi.org/10.1016/j.knosys.2016.11.022
• Benítez-Andrades, J.A., et al., 2022. Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study. JMIR Medical Informatics, 10, 2, 1–13. https://doi.org/10.2196/34492
• Berg, K.C., Peterson, C.B. and Frazier, P., 2012. Assessment and Diagnosis of Eating Disorders: A Guide for Professional Counselors. Journal of Counseling and Development, 90, 3, 262–269. https://doi.org/10.1002/j.1556-6676.2012.00033.x
• Bin Alam, M. S., Patwary, M. J. A., & Hassan, M. 2021. Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh. 2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021 - Proceedings, 95–99. https://doi.org/10.1109/ICICT4SD50815.2021.9396909
• Bulk, L.M., et al., 2022. Automatic Classification of Literature in Systematic Reviews on Food Safety Using Machine Learning. Current Research in Food Science, 5, 84–95. https://doi.org/10.1016/j.crfs.2021.12.010
• Cerasa, A. et al., 2015. Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results. Behavioural Neurology, 2015. https://doi.org/10.1155/2015/924814
• Desrivières, S. et al. 2024. (in review) Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder. Research Square, 1–23. https://doi.org/10.21203/rs.3.rs-3777784/v1
• Forrest, L.N., Ivezaj, V. and Grilo, C.M., 2021. Machine Learning v. Traditional Regression Models Predicting Treatment Outcomes for Binge-Eating Disorder from a Randomized Controlled Trial. Psychological Medicine, 1–12. https://doi.org/10.1017/S0033291721004748
• Gordon, G., Brockmeyer, T., Schmidt, U. and Campbell, C., 2019. Combining Cognitive Bias Modification Training (CBM) and Transcranial Direct Current Stimulation (TDCS) to Treat Binge Eating Disorder: Study Protocol of a Randomised Controlled Feasibility Trial. BMJ Open, 9, 10. https://doi.org/10.1136/bmjopen-2019-030023
• Güney, E. and Çepik Kuruoğlu, A., 2007. Yeme Bozukluklarında Beyin Görüntüleme Yöntemleri. Klinik Psikiyatri, 10, 93–101.
• Harrell, F.E., 2015. Binary Logistic Regression. In Regression Modeling Strategies, Cham, Switzerland: Springer Series in Statistics, Springer, 219–274. https://doi.org/10.1198/tech.2003.s158
• Hay, Phillipa et al., 2020. General Practitioner and Mental Healthcare Use in a Community Sample of People with Diagnostic Threshold Symptoms of Bulimia Nervosa, Binge-Eating Disorder, and Other Eating Disorders. International Journal of Eating Disorders, 53, 1, 61–68. https://doi.org/10.1002/eat.23174
• Henderson, C., and Freeman, M., 1987. A Self-Rating Scale for Bulimia the BITE. British Journal of Psychiatry, 150, 1, 18–24. https://doi.org/10.1192/bjp.150.1.18
• Hutson, P.H., Balodis, I.M. and Potenza, M.N., 2018. Binge-Eating Disorder: Clinical and Therapeutic Advances. Pharmacology and Therapeutics, 182, August 2017, 15–27. https://doi.org/10.1016/j.pharmthera.2017.08.002
• Karaca, B.K., Akşahin, M.F. and Öcal, R., 2019. EEG Tutarlılık Analizi Ile Multipl Skleroz Hastalığının Belirlenmesi. Tıp Teknolojileri Kongresi, 235–38.
• Kessler, R.C. et al., 2013. The Prevalence and Correlates of Binge Eating Disorder in the WHO World Mental Health Surveys. Biol Psychiatry, 73, 9, 904–1014. https://doi.org/10.1016/j.biopsych.2012.11.020
• Khehra, B.S., and Pharwaha, A.P.S., 2016. Classification of Clustered Microcalcifications Using MLFFBP-ANN and SVM. Egyptian Informatics Journal, 17, 1, 11–20. https://doi.org/10.1016/j.eij.2015.08.001
• Kober, H. and Boswell, R.G., 2018. Potential Psychological & Neural Mechanisms in Binge Eating Disorder: Implications for Treatment. Clinical Psychology Review, 60, December 2017, 32–44. https://doi.org/10.1016/j.cpr.2017.12.004
• Lewinsohn, P. M., Striegel-Moore, R. H. and Seeley, J. R., 2000. Epidemiology and Natural Course of Eating Disorders in Young Women from Adolescence to Young Adulthood. Journal of the American Academy of Child & Adolescent Psychiatry, 39, 10, 1284–1292. https://doi.org/10.1097/00004583-200010000-00016
• Linardon, J. et al., 2020. Interactions between Different Eating Patterns on Recurrent Binge-Eating Behavior: A Machine Learning Approach. International Journal of Eating Disorders, 53, 4, 533–540.
• Linardon, J., Fuller-tyszkiewicz, M. and Greenwood, C.J., 2022. An Exploratory Application of Machine Learning Methods to Optimize Prediction of Responsiveness to Digital Interventions for Eating Disorder Symptoms. International Journal of Eating Disorders, May, 1–6. https://doi.org/10.1002/eat.23733
• Merhbene, G., Puttick, A., & Kurpicz-Briki, M. 2024. Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature review. Frontiers in Psychiatry, 15(March), 1–15. https://doi.org/10.3389/fpsyt.2024.1319522
• Metlek, S., and Kayaalp, K., 2020. Derin Öğrenme ve Destek Vektör Makineleri Ile Görüntüden Cinsiyet Tahmini. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8, 2208–28. https://doi.org/10.29130/dubited.707316
• Orrù, G. and et al., 2021. A Machine Learning Analysis of Psychopathological Features of Eating Disorders: A Retrospective Study. Mediterranean Journal of Clinical Psychology, 9, 1, 1–19.
• Raab, D., Baumgartl, H. and Buettner, R., (2020). Machine Learning Based Diagnosis of Binge Eating Disorder Using EEG Recordings. Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020, 1–14.
• Ren, Y. et al., 2022. Using Machine Learning to Explore Core Risk Factors Associated with the Risk of Eating Disorders among Non-Clinical Young Women in China: A Decision-Tree Classification Analysis. Journal of Eating Disorders, 10, 1, 1–11. https://doi.org/10.1186/s40337-022-00545-6
• Sadeh-Sharvit, S., Fitzsimmons-Craft, E.E., Taylor, C.B. and Yom-Tov, E., 2020. Predicting Eating Disorders from Internet Activity. International Journal of Eating Disorders, 53, 9, 1526–1533. https://doi.org/10.1002/eat.23338
• Schapire, R.E. 2003. The Boosting Approach to Machine Learning: An Overview. In Nonlinear Estimation and Classification. Lecture Notes in Statistics, ed. B. Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu. NY: Springer. https://doi.org/10.1007/978-0-387-21579-2_9
• Sokolova, M., Japkowicz, N. and Szpakowicz, S., 2006. Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In AI 2006: Advances in Artificial Intelligence, ed. Bh. Sattar, A., Kang. Berlin: Springer Berlin Heidelberg, 1015–21.
• Sönmez, A.Ö., 2017. Çocuk ve Ergenlerde Yeme Bozuklukları. Psikiyatride Güncel Yaklaşımlar, 9, 3, 301–316. https://doi.org/10.18863/pgy.288643
• Turan, Ş., Aksoy-Poyraz, C. and Özdemir, A., 2015. Tıkınırcasına Yeme Bozukluğu. Psikiyatride Güncel Yaklaşımlar, 7, 4, 419–435. https://doi.org/10.5455/cap.20150213091928
• Türkmen, H., and Karaca-Sivrikaya, S., 2020. The Dietary Habits and Life Satisfaction According to the Food Groups Consumed by Young People. Progress in Nutrition, 22, 4, 1–10. https://doi.org/10.23751/pn.v22i4.8199
• Veranyurt, Ü., Deveci, A.F., Esen, M.F. and Veranyurt, O., 2020. Makine Öğrenmesi Teknikleriyle Hastalık Sınıflandırması: Random Forest, K-Nearest Neighbour Ve Adaboost Algoritmaları Uygulaması. Uuslararası Sağlık Yönetimi ve Stratejileri Araştırma Dergisi, 6, 2, 275–286.
• Vila-Blanco, N. et al., 2020. Deep Neural Networks for Chronological Age Estimation from OPG Images. IEEE Transactions on Medical Imaging, 39, 7, 2374–2384. https://doi.org/10.1109/TMI.2020.2968765
• Wang, S.B., 2021. Machine Learning to Advance the Prediction, Prevention and Treatment of Eating Disorders. European Eating Disorders Review, 29, 5, 683–691. https://doi.org/10.1002/erv.2850
• Wonderlich, S.A. et al., 2009. The Validity and Clinical Utility of Binge Eating Disorder. International Journal of Eating Disorders, 42, 8, 687–705. https://doi.org/10.1002/eat.20719
• Yan, H. et al., 2019. Automatic Detection of Eating Disorder-Related Social Media Posts That Could Benefit from a Mental Health Intervention. International Journal of Eating Disorders, 52, 10, 1150–1156. https://doi.org/10.1002/eat.23148
• Zhou, S. et al., 2020. Exploring Eating Disorder Topics on Twitter: Machine Learning Approach. JMIR Medical Informatics, 8, 10, 1–15. https://doi.org/10.2196/18273