用深度学习预测新生儿死亡风险，准确率达99%
A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
- 基于140万新生儿数据，用LSTM模型进行死亡风险预测
- LSTM模型准确率99%，优于其他机器学习方法
- 适合医疗决策支持系统，帮助早期干预高危婴儿
新生儿死亡仍是发展中国家乃至部分发达国家面临的严峻问题。据宏交易（Macro Trades）数据，全球每1000名出生婴儿中有26.693名在出生后死亡。为降低这一数字，及早预测高危新生儿至关重要，以便采取及时护理措施避免死亡。本研究利用机器学习方法判断新生儿是否处于风险中。基于140万例历史新生儿数据，采用逻辑回归、K近邻、随机森林分类器、极端梯度提升（XGBoost）、卷积神经网络和长短期记忆网络（LSTM）等模型进行训练，以识别最准确的预测模型。结果显示，在传统机器学习算法中，XGBoost与随机森林分类器表现最佳，准确率达94%；在深度学习模型中，LSTM准确率最高，达到99%。因此，使用LSTM是预测是否需要采取预防措施的最优方法。
原文摘要 · Abstract (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.
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