A deep learning and machine learning approach to predict neonatal death
  in the context of São Paulo
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.
6858b7c0c0c8e29df8ea3c29
Mohon Raihan
6858b7c0c0c8e29df8ea3c2a
Plabon Kumar Saha
6858b7c0c0c8e29df8ea3c2b
Rajan Das Gupta
claimed_verified
2025-06-23T08:15:03.417Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/JbaMFjmWFDd-tZmUxQ8aT.jpeg
Rajan Das Gupta
user
rajandasgupta
6858b7c0c0c8e29df8ea3c2c
A Z M Tahmidul Kabir
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Afia Anjum Tamanna
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Md. Harun-Ur-Rashid
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Adnan Bin Abdus Salam
6858b7c0c0c8e29df8ea3c30
Md Tanvir Anjum
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A Z M Ahteshamul Kabir
A deep learning and machine learning approach to predict neonatal death
  in the context of São Paulo
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.
Deep learning, specifically LSTM, outperforms other machine learning techniques in predicting neonatal mortality using historical data.
logical regression K-nearest neighbor random forest classifier extreme gradient boosting (XGBoost) convolutional neural network long short-term memory (LSTM)