Large Language Models in Computer Science Education: A Systematic Literature Review
Neonatal death is a major issue worldwide. Early prediction of at-risk babies can help prevent death. Using data from 1.4 million newborns, machine learning models like XGBoost, Random Forest, and LSTM were tested. XGBoost and Random Forest had 94% accuracy, while LSTM achieved 99% accuracy. LSTM is the most effective model for predicting neonatal mortality and guiding care.
Mar 1, 2024
A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
Neonatal death is a major issue worldwide. Early prediction of at-risk babies can help prevent death. Using data from 1.4 million newborns, machine learning models like XGBoost, Random Forest, and LSTM were tested. XGBoost and Random Forest had 94% accuracy, while LSTM achieved 99% accuracy. LSTM is the most effective model for predicting neonatal mortality and guiding care.
Dec 1, 2023