ABSTRACT Depression is a complex mental health condition influenced by a range of internal and external factors. This study presents a comprehensive mathematical model to predict depression, incorporating multiple theoretical frameworks. The model considers the linear relationship between symptoms and depression scores, the non‐linear effects of behavioral and emotional factors, time dependency in depression prediction, the impact of cognitive factors (latent variables), and the influence of social and environmental factors. The results show that symptom severity, cognitive factors, and social influences significantly affect depression scores. By combining these various factors, the model provides a more accurate and nuanced prediction of depression over time. The high R‐squared value indicates that the model explains a substantial portion of the variation in depression, confirming its robustness. This approach offers valuable insights into the dynamic and multifaceted nature of depression, providing a reliable tool for predicting depression and guiding mental health interventions and treatments.
Ganokratanaa et al. (Mon,) studied this question.