Research on Love Psychology Health Prediction based on XGBoost and Big Data

In the context of the growing prevalence of stress in modern society, the issue of mental health in romantic relationships has emerged as a significant concern. The analysis of large data sets pertaining to the mental health of individuals in romantic relationships enables the implementation of efficacious psychological interventions and support mechanisms. In this work, a personalized mental health prediction model based on multidimensional features was constructed. By optimizing the XGBoost algorithm, the model effectively processes high-dimensional and nonlinear data, and improves the accuracy and stability of prediction. At the same time, combined with the feature importance analysis of SHAP values, the interpretability of the model is enhanced and the key influencing factors are identified. In addition, the study comprehensively analyzes the complexity of mental health from multiple perspectives, provides customized mental health prediction and intervention programs for individuals, and realizes more accurate mental health management.