DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS

Authors : Mustafa Kemal Yöntem, Kemal Adem, Tahsin Ilhan, Serhat Kiliçarslan

Pages : 259-273

View : 45 | Download : 15

Publication Date : 2019-06-30

Article Type : Research

Abstract :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.
Keywords : Data mining, artificial neural networks, divorce, divorce prediction