基于深度学习与机器学习方法预测圣保罗地区新生儿死亡风险

摘要

新生儿死亡仍然是欠发达国家乃至部分发达国家所面临的严峻现实。根据Macro Trades的数据，全球范围内每千名新生儿中就有26.693名婴儿夭折。为降低这一数字，对高危婴儿的早期预测至关重要。此类预测为采取充分措施照护母婴提供了可能，从而避免婴儿早期死亡。在此背景下，机器学习被用于判断新生儿是否处于风险之中。为训练预测模型，研究采用了140万新生儿的历史数据。通过运用逻辑回归、K近邻算法、随机森林分类器、极端梯度提升（XGBoost）、卷积神经网络以及长短期记忆网络（LSTM）等机器学习和深度学习技术，基于该数据集识别出预测新生儿死亡率的最准确模型。在机器学习算法中，XGBoost与随机森林分类器以94%的准确率表现最佳；而在深度学习模型中，LSTM以99%的准确率位居榜首。因此，采用LSTM似乎是最为适宜的方法，用以判断是否需要对婴儿采取预防措施。

English

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.

基于深度学习与机器学习方法预测圣保罗地区新生儿死亡风险

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

摘要

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