Year 2019, Volume 9, Issue 1, Pages 259 - 273 2019-06-30

DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS

Mustafa Kemal Yöntem [1] , Kemal Adem [2] , Tahsin İlhan [3] , Serhat Kılıçarslan [4]

183 142

Within the scope of this research, the divorce prediction was carried out by using the Divorce Predictors Scale (DPS) on the basis of Gottman couples therapy. Of the participants, 84 (49%) were divorced and 86 (51%) were married couples. Participants completed the “Personal Information Form” and “Divorce Predictors Scale”. In this study, the success of DPS, was investigated using Multilayer Perceptron Neural Network and C4.5 Decision tree algorithms. In addition, the study also aims to find the most significant features/items in the Divorce Predictors Scale that affect the divorce. The most effective 6 features and their values of significance obtained by applying the correlation-based feature selection method on the divorce data set. When we look at these features, they are related to creating a common meaning and failed attempts to repair, love map and negative conflict behaviors. When the direct classification methods were applied to the divorce data set, the highest success rate was 98.23% obtained with the RBF neural network. After selecting the most effective 6 features using the correlation-based feature selection method on the same data set, the highest accuracy rate obtained was 98.82% with ANN. According to the results, DPS can predict divorce. Family counselors and family therapists can use this scale for contribute to the preparation of case formulation and intervention plan. Also it can be said that the divorce predictors in the Gottman couples therapy were confirmed in the Turkish sampling.

Data mining, artificial neural networks, divorce, divorce prediction
  • Babcock, J.C., Gottman, J., Ryan, K. & Gottman, J. (2013). A component analysis of a brief psycho‐educational couples' workshop: one‐year follow‐up results. Journal of Family Therapy 35(3): 252-280. https://doi.org/10.1111/1467-6427.12017
  • Baca-García, E., et al. (2006). Using data mining to explore complex clinical decisions: a study of hospitalization after a suicide attempt. Journal of Clinical Psychiatry, 67(7): 1124-1132. http://dx.doi.org/10.4088/JCP.v67n0716
  • Bae, S. M., Lee, S. H., Park, Y. M., Hyun, M. H., & Yoon, H. (2010). Predictive factors of social functioning in patients with schizophrenia: exploration for the best combination of variables using data mining. Psychiatry investigation, 7(2): 93-101. https://doi.org/10.4306/pi.2010.7.2.93
  • Barnacle, R. ES, & Abbott, D. A. (2009). The development and evaluation of a Gottman-based premarital education program: A pilot study. Journal of Couple & Relationship Therapy; 8(1): 64-82. https://doi.org/10.1080/15332690802626734
  • Çelik, E., Atalay, M. & Bayer, H. (2014). Yapay Sinir Ağları ve Destek Vektör Makineleri ile Deprem Tahmininde Sismik Darbelerin Kullanılması. IEEE 22nd Signal Processing and Communications Applications Conferance (SIU), 730-733.
  • Eriksson, R., Werge, T., Jensen, L. J., & Brunak, S. (2014). Dose-specific adverse drug reaction identification in electronic patient records: temporal data mining in an inpatient psychiatric population. Drug safety, 37(4): 237-247. http://dx.doi.org/10.1007/s40264-014-0158-7
  • Ertunc, H. M., Ocak, H. & Aliustaoglu, C. (2013). ANN-and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Computing and Applications, 22(1): 435-446. https://doi.org/10.1007/s00521-012-0912-7
  • Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. American Association for Artificial Intelligence Menlo Park, CA, USA.
  • Gottman, J. M. (1999). The marriage clinic: A scientifically-based marital therapy. WW Norton & Company.
  • Gottman, J. M. (2014). What predicts divorce? The relationship between marital processes and marital outcomes. Psychology Press.
  • Gottman, J. M. & Gottman, J.S. (2012). Çiftler arasında köprüyü inşa etmek: Gottman çift terapisi eğitimi 1. düzey kitabı, [Level 1 clinical training. Gottman Method Couples Therapy. Bringing to couple chasm.] İstanbul, Psikoloji İstanbul..
  • Gottman, J. ve Silver, N. (2014). Aşk nasıl sürdürülür. Aşk laboratuarından sırlar. (trans. Gül, S.S.) [What make love last. How to build trust and avoid betrayal. 2012]. İstanbul, Varlık Yayınları,.3.
  • Gottman, J. & Silver, N. (2015). Evliliği sürdürmenin yedi ilkesi.(trans. Gül, S.S.). [The seven principles for making marriage work. 1999] İstanbul, Varlık Yayınları.
  • Hall, M. (1999). Correlation-Based Feature Selection for Machine Learning. Phd Thesis, Department Of Computer Science, Waikato University, New Zealand, 26-28.
  • Hall, M. A. & Smith, L. A. (1998). Practical feature subset selection for machine learning. In Computer science’98 proceedings of the 21st Australasian computer science conference ACSC, 98: 181-191.
  • Han, J., Kamber, M. & Pei, J. (2006). Data mining: concepts and techniques. The Morgan Kaufmann Series İn Data Management Systems. Morgan Kaufmann Publishers, 230-240.
  • Holman, T. B. & Jarvis, M. O. (2003). Hostile, volatile, avoiding, and validating couple‐conflict types: An investigation of Gottman's couple‐conflict types. Personal Relationships; 10(2): 267-282. http://dx.doi.org/10.1111/1475-6811.00049
  • Karaatlı, M., Helvacıoğlu, Ö. C., Ömürbek, N. & Tokgöz, G. (2012). Yapay Sinir Ağları Yöntemi İle Otomobil Satış Tahmini. Uluslararası Yönetim İktisat ve İşletme Dergisi, 8(17): 87-100. http://dx.doi.org/10.11122/ijmeb.2012.8.17.290
  • Karahan, M. (2015). Turizm talebinin Yapay Sinir Ağları yöntemiyle tahmin edilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2015; 20(2): 195-209.
  • Kaynar, O., Aydın, Z. & Görmez, Y. (2017). Sentiment Analizinde Öznitelik Düşürme Yöntemlerinin Oto Kodlayıcılı Derin Öğrenme Makinaları ile Karşılaştırılması. Bilişim Teknolojileri Dergisi, 10(3): 319-326. http://dx.doi.org/10.17671/gazibtd.331046
  • Kaynar, O., Taştan, S. & Demirkoparan, F. (2011). Yapay sinir ağları ile doğalgaz tüketim tahmini. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 25.
  • Nguyên, X., Chaskalovis, J., Rakotonanahary, D. & Fleury, B. (2010). Insomnia symptoms and CPAP compliance in OSAS patients: A descriptive study using Data Mining methods. Sleep medicine 11(8): 777-784. https://doi.org/10.1016/j.sleep.2010.04.008
  • Qinghua, J. (2016). Data Mining and Management System Design and Application for College Student Mental Health. Intelligent Transportation, Big Data & Smart City (ICITBS), 2016 International Conference on. IEEE: 410-413. http://dx.doi.org/10.1109/ICITBS.2016.96
  • Rosenthal, D. A., Dalton, J. A., & Gervey, R. (2007). Analyzing vocational outcomes of individuals with psychiatric disabilities who received state vocational rehabilitation services: A data mining approach. International Journal of Social Psychiatry , 53(4): 357-368. https://doi.org/10.1177/0020764006074555
  • Schalkoff, R. J. (1997). Artificial neural networks (Vol. 1). New York: McGraw-Hill.
  • Song, Q. (2010). The comparison and analysis of classification methods for psychological assessment data. Information Science and Engineering (ICISE), 2010 2nd International Conference on. IEEE. 4133-4135. http://dx.doi.org/10.1109/ICISE.2010.5690602
  • Shapiro, A. & Gottman, J. (2005). Effects on marriage of a psycho-communicative-educational intervention with couples undergoing the transition to parenthood, evaluation at 1-year post intervention. Journal Of Family Communication; 5(1): 1-24. https://doi.org/10.1207/s15327698jfc0501_1
  • Shapiro, A. F., Nahm, E. Y., Gottman, J. M., & Content, K. (2011). Bringing baby home together: Examining the impact of a couple‐focused intervention on the dynamics within family play. American Journal of Orthopsychiatry; 81(3): 337. http://dx.doi.org/10.1111/j.1939-0025.2011.01102.x
  • Uğur, A. & Kınacı, A. C. (2006). Yapay zeka teknikleri ve yapay sinir ağları kullanılarak web sayfalarının sınıflandırılması. XI. Türkiye'de İnternet Konferansı (inet-tr'06), 1-4.
  • Yöntem, M.K. & İlhan, T.(2018). Boşanma göstergeleri ölçeği: güvenirlik ve geçerlik çalışması. X. Uluslararası Eğitim Araştırmaları Kongres. Nevsehir, Turkey .9.
  • Yöntem, M.K. & İlhan, T. (2018). Boşanma göstergeleri ölçeğinin geliştirilmesi. [Development of the divorce predictors scale]. Sosyal Polika Çalışmaları Dergisi. 41. 10. Yöntem, M. K., Adem, K., İlhan, T., & Kılıçarslan, S. (2018). Çok Katmanlı Algılayıcı Sinir Ağı ve C4. 5 Karar ağacı algoritmaları ile Boşanma Tahmini. International Congress on Politic, Economic and Social Studies (ICPESS) 2018:4.
Primary Language tr
Subjects Social
Journal Section Full Issue
Authors

Orcid: 0000-0001-7620-0971
Author: Mustafa Kemal Yöntem (Primary Author)
Institution: NEVŞEHİR HACI BEKTAŞ VELİ ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ
Country: Turkey


Orcid: 0000-0002-3752-7354
Author: Kemal Adem
Institution: AKSARAY ÜNİVERSİTESİ

Orcid: 0000-0002-5007-5022
Author: Tahsin İlhan
Institution: TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ

Orcid: 0000-0001-9483-4425
Author: Serhat Kılıçarslan

Dates

Publication Date: June 30, 2019

Bibtex @research article { nevsosbilen549416, journal = {Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi}, issn = {}, eissn = {2149-3871}, address = {Nevşehir Hacı Bektaş Veli Üniversitesi}, year = {2019}, volume = {9}, pages = {259 - 273}, doi = {}, title = {DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS}, key = {cite}, author = {Yöntem, Mustafa Kemal and Adem, Kemal and İlhan, Tahsin and Kılıçarslan, Serhat} }
APA Yöntem, M , Adem, K , İlhan, T , Kılıçarslan, S . (2019). DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 9 (1), 259-273. Retrieved from http://dergipark.org.tr/nevsosbilen/issue/46568/549416
MLA Yöntem, M , Adem, K , İlhan, T , Kılıçarslan, S . "DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS". Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi 9 (2019): 259-273 <http://dergipark.org.tr/nevsosbilen/issue/46568/549416>
Chicago Yöntem, M , Adem, K , İlhan, T , Kılıçarslan, S . "DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS". Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi 9 (2019): 259-273
RIS TY - JOUR T1 - DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS AU - Mustafa Kemal Yöntem , Kemal Adem , Tahsin İlhan , Serhat Kılıçarslan Y1 - 2019 PY - 2019 N1 - DO - T2 - Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi JF - Journal JO - JOR SP - 259 EP - 273 VL - 9 IS - 1 SN - -2149-3871 M3 - UR - Y2 - 2019 ER -
EndNote %0 Nevşehir Hacı Bektaş Veli University Journal of ISS DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS %A Mustafa Kemal Yöntem , Kemal Adem , Tahsin İlhan , Serhat Kılıçarslan %T DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS %D 2019 %J Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi %P -2149-3871 %V 9 %N 1 %R %U
ISNAD Yöntem, Mustafa Kemal , Adem, Kemal , İlhan, Tahsin , Kılıçarslan, Serhat . "DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS". Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi 9 / 1 (June 2019): 259-273.
AMA Yöntem M , Adem K , İlhan T , Kılıçarslan S . DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi. 2019; 9(1): 259-273.
Vancouver Yöntem M , Adem K , İlhan T , Kılıçarslan S . DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi. 2019; 9(1): 273-259.