Developing an Interpretable Machine Learning Model for Divorce Prediction

Divorce is a legal process that formally ends a marital union between two individuals. In modern times, it is considered one of the major social issues which is rapidly increasing day by day. It not only terminates the relationship between two persons but also affects the harmony of their family members and other associated people. The aim of this work is to propose an Explainable AI (XAI)-based model that extracts significant factors of divorced/happy couples based on the outcomes of the best classifier. In this work, we gathered a divorced predictor dataset from the University of California Irvine (UCI) machine learning repository. This dataset was subsequently preprocessed and employed different classifiers to identify divorced/happy couples more precisely. After evaluating the performance of individual classifiers, SVM showed the best classification performance where its maximum accuracy is 98.23%. Then, we considered one of the most used XAI models called Shapley Additive Explanations (SHAP) to employ SVM for representing which individual feature values are responsible for identifying happy couples/divorce cases. Thus, A40 is found as the most important feature using SHAP analysis.