Diagnosis of hypertension in minors using RNA (2018)

This project addresses the problem of arterial hypertension, with particular emphasis on its early diagnosis in the pediatric population, where clinical assessment is complex due to age, sex, and height dependent reference values. Drawing on expert knowledge from cardiovascular medicine, pediatrics, and clinical risk assessment, the study identifies and integrates key genetic, behavioral, clinical, and laboratory variables associated with hypertension and its long term complications. The work applies artificial intelligence expertise through the development of an Artificial Neural Network model based on the Multilayer Perceptron algorithm, framing medical diagnosis as a supervised classification problem. By combining domain specific medical criteria with advanced machine learning techniques, the proposed model seeks to maximize diagnostic accuracy and support early identification and timely intervention in children and adolescents at risk of hypertension.