ESTIMATION OF DEPRESSION DISEASE BY NEURAL FUZZY INFERENCE METHODD

Year 2019, Volume: 1 Issue: 2, 49 - 58, 16.12.2019

Zekeriya Akçay , Remzi Gürfidan

Abstract

In this study, an ANFIS model that analyzes the data set of depression disease was established and the degree of disease was estimated in this study which was performed for the detection of depression disease by neural fuzzy logic inference method (ANFIS). The data set was prepared according to Beck Depression Test results. The Neuro Fuzzy Designer included in Matlab R2016b was used to process the data set using ANFIS method and generate estimation values. In ANFIS, sugeno method was used and Trimf was selected as the activation function. The learning method is the backpropa method known as the back propagation method. At the end of 50 trainings, the training error value was determined as 0.018197. The training error value resulting from processing a total of 200 disease records is in good condition. The actual values and the estimated values produced by the system were analyzed with SPSS Statistics software and standard deviation and error values were determined. The system is aimed to classify the degree of disease correctly according to the symptoms.

Keywords

ANFIS, Fuzzy Logic, , Artificial Neural Network, Depression, Disease

References

• Karamustafalioğlu, O., Yumrukçal, H., (2011). “Depresyon ve Anksiyete Bozuklukları”, Şişli Etfal Hospital Medical Bulletin,45(2).
• Uğurlu, T.T., Şengül, C.B., Şengül, C., (2012). ”Bağımlılık Psikofarmakolojisi”, Psikiyatride Güncel Yaklaşımlar;4(1):37-50.
• Haznedar, B., Kalinli, A., (2016). “Determination of The Relationship Between Thrombophilia Disease and Genetic Disorders with Adaptive Network Based Fuzzy Logic Inference System (ANFIS) SA, SAU Fen Bil Der 20(1), 13-21.
• Arslan, M.T., Haznedar, B., (2016). “Classification of Prostate Cancer Gene Expression Profile by ANFIS”, International Mediterranean Science and Engineering Congress (IMSEC 2016).
• Kaya, H., Akciğer Hastalıkları Teşhisinde Sınıflandırma Ve Bulanık Mantık Yöntemlerinin Uygulanması, Ankara University, Department of Computer Engineering, M.Sc. Thesis, Ankara
• Gökçe, B., Sonugür, G., (2016). ANFIS ve YSA Yöntemleri ile İşlenmiş Doğal Taş Üretim Sürecinde Verimlilik Analizi, Afyon Kocatepe University Journal of Science and Engineering, 74-185, DOI: 10.5578 / fmbd.13951
• Bozkurt, N., (2004). Bir Grup Üniversite Öğrencisinin Depresyon ve Kaygı Düzeyleri ile Çeşitli Değişkenler Arasındaki İlişkiler,29 (133), 32-39.
• Yücel, A. (2010), Tedarikçi Seçimi Probleminde Bütünleşik Sinirsel Bulanık Mantık Yaklaşımı, Istanbul University, Department of Industrial Engineering, PhD Thesis, Istanbul.
• Jang, J.-S. R. (1993), F ANFIS: Adaptive-Network-Based Fuzzy Inference System ”, IEEE Transactions on Systems, Man and Cybernetics, 23 (3), 665-685.
• Şen Z. (2004), Mühendislikte Bulanık Mantık (Fuzzy) İle Modelleme Prensipleri, Water Foundation Publications, Istanbul.
• Doğan, O., (2016). Uyarlamalı Sinirsel Bulanık Çıkarım Sisteminin (ANFIS) Talep Tahmini İçin Kullanımı ve Bir Uygulama”, Dokuz Eylül University Journal of Economics and Administrative Sciences, Volume: 31, No: 1 ss: 257-288
• Beck, A. T., Ward, C., Mendelson, M., Mock, J., & Erbaugh, J. (1961). Beck depression inventory (BDI). Arch Gen Psychiatry, 4(6), 561-571.
• García-Batista, Z. E., Guerra-Peña, K., Cano-Vindel, A., Herrera-Martínez, S. X., & Medrano, L. A. (2018). Validity and reliability of the Beck Depression Inventory (BDI-II) in general and hospital population of Dominican Republic. PloS one, 13(6), e0199750.
• Süzen, A. A. (2019). LSTM Derin Sinir Ağları İle Üniversite Giriş Sınavındaki Matematik Soru Sayılarının Konulara Göre Tahmini. Engineering Sciences , 14 (3) , 112-118 . http://dx.doi.org/10.12739/NWSA.2019.14.3.1A0436
• Süzen, A. A., & Kayaalp, K. (2018). Forecasting Temperature with Deep Learning Methods Samples of Isparta Province, International Academic Research Congress, 531-537.