A deep learning and machine learning approach to predict neonatal death in the context of São Paulo

Mohon Raihan, Plabon Kumar Saha, Rajan Das Gupta, A Z M Tahmidul Kabir, Afia Anjum Tamanna, MD. Harun-Ur-Rashid, Adnan Bin Abdus Salam, MD Tanvir Anjum, A Z M Ahteshamul Kabir


Neonatal death is still a concerning reality for underdeveloped and even for some of the developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births according to Macro Trades. To reduce the death early prediction of endangered baby is crucial. An early prediction enables the opportunity to take ample care of the child and mother so that an early child death can be avoided. Machine learning was used to figure out whether a newborn baby is at risk. To train the predictive model historical data of 1.4 million newborn child data 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, long short-term memory (LSTM). were implemented using the dataset to find out the most robust model which model is the most accurate to identify the mortality of a newborn. From all the machine learning algorithms, the XGboost and random classifier had the best accuracy with 94%, and from the deep learning model, the LSTM had the best outcome with 99% accuracy. Thus, using LSTM of the model shall be best suited to predict whether precaution for a child is necessary.

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DOI: http://doi.org/10.11591/ijphs.v13i1.22577


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International Journal of Public Health Science (IJPHS)
p-ISSN: 2252-8806, e-ISSN: 2620-4126

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