Research Area:  Machine Learning

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

Author(s) Name:  Songhee Cheon, Jungyoon Kim, Jihye Lim

Publisher name:  MDPI

DOI:  10.3390/ijerph16111876

Volume Information:  Volume 16

Paper Link:   https://www.mdpi.com/1660-4601/16/11/1876