The Use of Deep Learning to Predict Stroke Patient Mortality

Songhee Cheon, Jungyoon Kim and Jihye Lim
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Songhee Cheon: Department of Physical Therapy, Youngsan University, Yangsan 626-790, Korea
Jungyoon Kim: Department of Computer Science, Kent State University, Kent, OH 44242, USA
Jihye Lim: Department of Healthcare Management, Youngsan University, Yangsan 626-790, Korea

IJERPH, 2019, vol. 16, issue 11, 1-12

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.

Keywords: stroke; prediction; deep learning; feature extraction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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