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Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database
Abstract
Electronic medical claims (EMCs) can be used to accurately predict the occurrence of a variety of diseases, which can contribute to precise medical interventions. While there is a growing interest in the application of machine learning (ML) techniques to address clinical problems, the use of deep-learning in healthcare have just gained attention recently. Deep learning, such as deep neural network (DNN), has achieved impressive results in the areas of speech recognition, computer vision, and natural language processing in recent years. However, deep learning is often difficult to comprehend due to the complexities in its framework. Furthermore, this method has not yet been demonstrated to achieve a better performance comparing to other conventional ML algorithms in disease prediction tasks using EMCs. In this study, we utilize a large population-based EMC database of around 800,000 patients to compare DNN with three other ML approaches for predicting 5-year stroke occurrence. The result shows that DNN and gradient boosting decision tree (GBDT) can result in similarly high prediction accuracies that are better compared to logistic regression (LR) and support vector machine (SVM) approaches. Meanwhile, DNN achieves optimal results by using lesser amounts of patient data when comparing to GBDT method.
Figures
The receiver operating characteristic curve and AUC for the predictive performance of DNN and GBDT
The receiver operating characteristic curve and AUC for the predictive performance of DNN and GBDT
Performance of DNN, GBDT, LR and SVM models with different training data amount
Performance of DNN, GBDT, LR and SVM models with different training data amount
Authors
Publication Date
2017/07/11
Conference
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI
10.1109/embc.2017.8037515
Publisher
IEEE