Divorce Prediction Using Machine Learning Algorithms in Ha'il Region, KSA

Abstract

This paper presents a study in which the divorce predictor scale (DPS), based on Gottman couples therapy, was used to predict divorce. The 54 items in the DPS were used as features or attributes in a machine learning model. In addition to the DPS, a personal information form was utilized to gather participants' personal data in order to conduct this study in a more structured and traditional manner. Out of 148 participants 116 participants were married whereas 32 were divorced. With the use of algorithms Artificial Neural Network (ANN), Naïve Byes (NB) and Random Forest (RF), the effectiveness of DPS was examined in this study. The correlation based feature selection method was used to identify the top 6 features from the same dataset and the highest accuracy rate was 96.61\% with ANN. The results show that DPS can predict divorce. This scale can help family counselors and therapists in case formulation and intervention plan development process. Additionally, it may be argued that the Hail region, KSA sampling confirmed the Gottman couples treatment predictors.