Artificial Intelligence Based Approach to Self-Sensitivity and Compassion Scores: Development of Prediction Models

Abstract

This study investigates the usability of artificial intelligence and machine learning techniques to predict individuals' levels of self-sensitivity and compassion. As self-sensitivity and compassion significantly affect individuals' ability to cope with stress, this study aims to develop models to help effectively measure these psychosocial variables. The research covers Gaussian Process Regression (GPR), Neural Network Regression (Net), and Support Vector Machine (SVM) Regression models. The data were collected using the self-sensitivity and compassion scales, and MAPE, MAE, SE, MSE, RMSE, R, and R² values were used as performance evaluation criteria for each model. The findings show that the GPR model provides highly accurate predictions for both scale types. The Net and SVM models also provided effective predictions, but GPR performed the best overall. Artificial intelligence and machine learning-based models have emerged as practical tools for predicting self-sensitivity and compassion scores. The GPR model is particularly notable for its high prediction accuracy. These findings offer important applications in nursing practice and the design of psychosocial interventions.