Prioritization to address possible cases of clinical depression by applying the GBT+ algorithm

Abstract

Major depression represents a critical challenge in public health due to its high prevalence and impact on patients’ quality of life. This study proposes a predictive model based on Gradient Boosting Trees (GBT) to prioritize clinical care for patients with potential major depression in Lima, Peru. A dataset with 10,000 clinical records was used, including demographic, behavioral, and medical variables, such as sleep patterns, family history, and lifestyle habits. The applied methodology comprises data preprocessing, feature selection, hyperparameter optimization, and validation using metrics such as AUC-ROC, accuracy, and F1-score. The model obtained an accuracy of 89.7%, an AUC-ROC of 0.92, and a 30% reduction in diagnostic time compared to traditional methods. In addition, it allowed a 35% improvement in the identification of high-risk patients, optimizing the allocation of medical resources. These results demonstrate that the use of machine learning in mental health can significantly improve the efficiency of depression detection and treatment. The implementation of this model in hospitals and public health centers is recommended to strengthen clinical decision-making and ensure more equitable and evidencebased care.