Development of an auxiliary model for the diagnosis of sudden infant death syndrome considering neonatal sleep apnea as a risk factor

Sudden Infant Death Syndrome (SIDS) is a serious public health concern, representing one of the leading causes of mortality in infants during their first year of life. This study focuses on investigating the possible relationship between sleep apnea and SIDS, addressing factors such as birth weight, age, and gender of the neonates. Using a methodology of data mining and recurrent neural networks (RNNs), physiological data obtained during the first 12 hours of life were analyzed. Initial results showed limited correlations, but with deeper analysis, greater consistency and accuracy in apnea predictions were achieved, reaching high reliability in the RNN model. Collaboration with the Mexican Red Cross was crucial, providing valuable data and allowing for the reevaluation of neonatal monitoring protocols. This methodological approach not only enhances the detection and early prevention of SIDS based on sleep apnea but also suggests practical solutions to reduce the incidence of this tragic condition.