Clinical Stress Level Prediction Using Metabolic Biomarkers and Genetic Algorithm–Based Machine Learning Models

Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine learning models based on genetic algorithms. Data were obtained from 87 university students, including measurements of glucose, insulin, and cortisol, as well as perceived stress scores assessed using the Perceived Stress Scale (PSS). Stress levels were categorized into low (n=5), moderate (n=22), and high (n=60) classes, reflecting an imbalanced dataset. Feature engineering and genetic algorithm–based selection identified glucose concentration, the insulin–glucose ratio, and the insulin–cortisol ratio as the most relevant features. These were used to train XGBoost and Elastic Net models, which were evaluated using leave-one-out cross-validation. The XGBoost model achieved the best performance, with an accuracy of 0.77 and strong predictive capability for high stress levels. The results demonstrate the usefulness of machine learning based on metabolic biomarkers as an objective tool for stress assessment in psychological and clinical research.