A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
This work addresses neonatal mortality prediction in São Paulo, offering a potential tool for early intervention, though it is incremental as it applies existing methods to a specific dataset.
The paper tackled predicting neonatal death using machine and deep learning on a dataset of 1.4 million newborns, finding that LSTM achieved 99% accuracy, outperforming other models like XGBoost and random forest at 94%.
Neonatal death is still a concerning reality for underdeveloped and even some developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births die, according to Macro Trades. To reduce this number, early prediction of endangered babies is crucial. Such prediction enables the opportunity to take ample care of the child and mother so that early child death can be avoided. In this context, machine learning was used to determine whether a newborn baby is at risk. To train the predictive model, historical data of 1.4 million newborns was used. Machine learning and deep learning techniques such as logical regression, K-nearest neighbor, random forest classifier, extreme gradient boosting (XGBoost), convolutional neural network, and long short-term memory (LSTM) were implemented using the dataset to identify the most accurate model for predicting neonatal mortality. Among the machine learning algorithms, XGBoost and random forest classifier achieved the best accuracy with 94%, while among the deep learning models, LSTM delivered the highest accuracy with 99%. Therefore, using LSTM appears to be the most suitable approach to predict whether precautionary measures for a child are necessary.