Machine learning model to predict the divorce of a married couple

Divorce usually impacts the closest family members, over the years the divorce rate has increased dramatically, especially in the last two decades and worsening with the pandemic, where there has been a significant increase in the divorce rate in many countries of the world. We draw on Yontem's work where he poses 56 questions as predictors of divorce. In addition, we make use of 4 automatic learning models (perceptron, logistic regression, neural networks and randomized forest) and 3 hybrid models based on voting criteria. Each of these models was trained in 5 different scenarios, making a total of 35 experiments, the best performance obtained in terms of precision, sensitivity and specificity is 0.9853, 1.0 and 0.9667 respectively, corresponding to the perceptron model and a hybrid model; however, although the results show a high performance, the context, the amount of data and the country in which the data were collected must be considered.