A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data

Jungyoon Kim and Jihye Lim
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Jungyoon Kim: Department of Computer Science, Kent State University, Kent, OH 44242, USA
Jihye Lim: Department of Health Care and Science, Donga University, Nakdong-Daero 550 beongil 37, Saha-Gu, Busan 49315, Korea

IJERPH, 2021, vol. 18, issue 10, 1-13

Abstract: The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.

Keywords: deep learning; deep neural network; dementia; feature extraction; prediction; principal component analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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