Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination.

Woo Jung Kim,Eun Lee,Ji Min Sung,Kee Namkoong,Hyuk-Jae Chang,Myeong-Hun Chae,Suk Kyoon An,David Sung

doi:10.2196/13139

Abstract

BackgroundWith the increase in the world’s aging population, there is a growing need to prevent and predict dementia among the general population. The availability of national time-series health examination data in South Korea provides an opportunity to use deep learning algorithm, an artificial intelligence technology, to expedite the analysis of mass and sequential data.ObjectiveThis study aimed to compare the discriminative accuracy between a time-series deep learning algorithm and conventional statistical methods to predict all-cause dementia and Alzheimer dementia using periodic health examination data.MethodsDiagnostic codes in medical claims data from a South Korean national health examination cohort were used to identify individuals who developed dementia or Alzheimer dementia over a 10-year period. As a result, 479,845 and 465,081 individuals, who were aged 40 to 79 years and without all-cause dementia and Alzheimer dementia, respectively, were identified at baseline. The performance of the following 3 models was compared with predictions of which individuals would develop either type of dementia: Cox proportional hazards model using only baseline data (HR-B), Cox proportional hazards model using repeated measurements (HR-R), and deep learning model using repeated measurements (DL-R).ResultsThe discrimination indices (95% CI) for the HR-B, HR-R, and DL-R models to predict all-cause dementia were 0.84 (0.83-0.85), 0.87 (0.86-0.88), and 0.90 (0.90-0.90), respectively, and those to predict Alzheimer dementia were 0.87 (0.86-0.88), 0.90 (0.88-0.91), and 0.91 (0.91-0.91), respectively. The DL-R model showed the best performance, followed by the HR-R model, in predicting both types of dementia. The DL-R model was superior to the HR-R model in all validation groups tested.ConclusionsA deep learning algorithm using time-series data can be an accurate and cost-effective method to predict dementia. A combination of deep learning and proportional hazards models might help to enhance prevention strategies for dementia.

Highlights

BackgroundThe prevention of dementia is a public health challenge in countries with aging populations [1]
The hazards model using only baseline data (HR-B) models for both all-cause dementia and Alzheimer dementia identified the following risk factors: age, female sex, systolic blood pressure (SBP), fasting glucose, cardiovascular disease, diabetes, psychiatric disorder, and neurological disorder
The risk factors identified by the hazards model using repeated measurements (HR-R) models for both all-cause dementia and Alzheimer dementia were age, female sex, no exercise, cardiovascular disease, diabetes, psychiatric disorder, and neurological disorder

Summary

Introduction

BackgroundThe prevention of dementia is a public health challenge in countries with aging populations [1]. In South Korea, a country with one of the fastest-growing elderly populations [8], the National Health Insurance Service (NHIS) runs periodic general health examination programs [9]. The large time-series health examination dataset established by the NHIS includes lifestyle information and the results of periodic routine medical examinations of a nationwide sample of the Korean population. Among the deep learning algorithms, the recurrent neural network (RNN) is considered the most suitable method for analyzing time-series data [15,16]. The availability of national time-series health examination data in South Korea provides an opportunity to use deep learning algorithm, an artificial intelligence technology, to expedite the analysis of mass and sequential data

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Discussion

Conclusion