Abstract
Dementia is a cognitive impairment that poses a global threat. Current dementia treatments slow the progression of the disease. The timing of starting such treatment markedly affects the effectiveness of the treatment. Some experts mentioned that the optimal timing for starting the currently available treatment in order to delay progression to dementia is the mild cognitive impairment stage, which is the prior stage of dementia. However, medical records are typically only available at a later stage, i.e., from the early or middle stage of dementia. In order to address this limitation, this study developed a model using national health information data from 5 years prior, to predict dementia development 5 years in the future. The Senior Cohort Database, comprising 550,000 samples, were used for model development. The F-measure of the model predicting dementia development after a 5-year incubation period was 77.38%. Models for a 1- and 3-year incubation period were also developed for comparative analysis of dementia risk factors. The three models had some risk factors in common, but also had unique risk factors, depending on the stage. For the common risk factors, a difference in disease severity was confirmed. These findings indicate that the diagnostic criteria and treatment strategy for dementia should differ depending on the timing. Furthermore, since the results of this study present new dementia risk factors that have not been reported previously, this study may also contribute to identification of new dementia risk factors.
Highlights
With the aging of the population, early diagnosis and timely treatment of dementia are among the key focus areas in medicine
The characteristics of the three algorithms that showed the best performance among various classification models, i.e., random forest, support vector machine (SVM), and multi-layer perceptron (MLP), are outlined
Since forward propagation is sufficient for National Health Insurance Service (NHIS) health information data without the need for back propagation, the performance with MLP modeling was slightly lower than that of other algorithms
Summary
With the aging of the population, early diagnosis and timely treatment of dementia are among the key focus areas in medicine. Treatment of dementia can slow the disease progression, whereas a delay in treatment leads to reduced efficacy of medication and shortens the period during which the patient can benefit from the effect of treatment [1]. It has been concluded that the earliest point of diagnosis at which treatment is effective is mild cognitive impairment (MCI), which is considered to represent the early stage of dementia [2]. Several studies have developed techniques for the early diagnosis of dementia based on brain magnetic resonance imaging (MRI), which has presented excellent performance, with area under the ROC curve (AUC) values of 98% for Alzheimer’s disease and 87% for MCI in a previous study [5]. Positron emission tomography (PET), the most commonly employed neuroimaging tool for dementia diagnosis, can demonstrate neurometabolic
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