AI-Guided Regulation of Catecholamines for Predicting Post-Treatment Recovery in Depression and Neurological Disorders

Abstract

Using catecholamine biomarkers, the study offers an AI-guided approach for forecasting posttreatment recovery in depressed patients with neurological diseases. Relevant patterns connected with recovery are derived by examining biochemical profiles—namely, dopamine, norepinephrine, and epinephrine levels. Supervised machine learning techniques—including Multi-Layer Perceptron (MLP) for classification and Random Forest Regression for forecasting recovery length—are included in the approach. With total catecholamines obtained as a key feature, a dataset of 500 patients was reviewed. With mediocre performance, especially in identifying moderate cases, the MLP classifier found depression severity. Random Forest Regression provided a superior fit for recovery time prediction, reaching an R² score of 0.86. Feature importance analysis emphasized total catecholamines as the most important factor. The findings reveal that using artificial intelligence models with biochemical analysis offers insightful information for customized treatment R2 value of ensemble model is 0.90. Early detection of recovery patterns is also made possible by the model, therefore assisting clinical judgments. Realtime prognostic support is shown in the framework's potential for integration into healthcare systems.