Prediction of the End of a Romantic Relationship in Peruvian Youth and Adults: A Machine Learning Approach.

Abstract

This study explores the effectiveness of machine learning models in predicting the end of romantic relationships among Peruvian youth and adults, considering various socioeconomic and personal attributes. The study implements logistic regression, gradient boosting, support vector machines, and decision trees on SMOTE-balanced data using a sample of 429 individuals to improve model robustness and accuracy. Using stratified random sampling, the data is split into training (80%) and validation (20%) sets. The models are evaluated through 10-fold cross-validation, focusing on accuracy, F1-score, AUC, sensitivity, and specificity metrics. The Random Forest model is the preferred algorithm because of its superior performance in all evaluation metrics. Hyperparameter tuning was conducted to optimize the model, identifying key predictors of relationship dissolution, including negative interactions, desire for emotional infidelity, and low relationship satisfaction. SHAP analysis was utilized to interpret the directional impact of each variable on the prediction outcomes. This study underscores the potential of machine learning tools in providing deep insights into relationship dynamics, suggesting their application in personalized therapeutic interventions to enhance relationship quality and reduce the incidence of breakups. Future research should incorporate larger and more diverse datasets to further validate these findings.