DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS

Yıl 2019, Cilt: 9 Sayı: 1, 259 - 273, 30.06.2019

Mustafa Kemal Yöntem , Kemal Adem , Tahsin İlhan , Serhat Kılıçarslan

Öz

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.

Anahtar Kelimeler

Data mining, artificial neural networks, divorce, divorce prediction

Kaynakça

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