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The Use of Deep Learning to Predict Stroke Patient Mortality

1
Department of Physical Therapy, Youngsan University, Yangsan 626-790, Korea
2
Department of Computer Science, Kent State University, Kent, OH 44242, USA
3
Department of Healthcare Management, Youngsan University, Yangsan 626-790, Korea
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(11), 1876; https://doi.org/10.3390/ijerph16111876
Received: 30 April 2019 / Revised: 23 May 2019 / Accepted: 24 May 2019 / Published: 28 May 2019
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Abstract

The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Timely treatment can improve stroke prognosis. Awareness of stroke warning signs and appropriate actions in the event of a stroke improve outcomes. Medical service use and health behavior data are easier to collect than medical imaging data. Here, we used a deep neural network to detect stroke using medical service use and health behavior data; we identified 15,099 patients with stroke. Principal component analysis (PCA) featuring quantile scaling was used to extract relevant background features from medical records; we used these to predict stroke. We compared our method (a scaled PCA/deep neural network [DNN] approach) to five other machine-learning methods. The area under the curve (AUC) value of our method was 83.48%; hence; it can be used by both patients and doctors to prescreen for possible stroke. View Full-Text
Keywords: stroke; prediction; deep learning; feature extraction stroke; prediction; deep learning; feature extraction
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Cheon, S.; Kim, J.; Lim, J. The Use of Deep Learning to Predict Stroke Patient Mortality. Int. J. Environ. Res. Public Health 2019, 16, 1876.

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