A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
This study addresses neonatal mortality risk prediction in the São Paulo region using a large-scale, real-world birth dataset comprising 1.4 million records. To enable early identification of high-risk newborns, we establish a multi-model comparative framework and—novelty for this region—introduce Long Short-Term Memory (LSTM) networks for neonatal mortality prediction, overcoming performance limitations of conventional machine learning approaches. Experimental results demonstrate that the LSTM model achieves 99% accuracy, substantially outperforming baseline models including logistic regression, k-nearest neighbors (KNN), random forest (94%), XGBoost (94%), and convolutional neural networks (CNN). The findings empirically validate the efficacy of temporal modeling for perinatal risk prediction and yield a clinically deployable, high-accuracy early-warning system. This work establishes a new paradigm for neonatal mortality intervention in resource-constrained settings.