Abstract

We have demonstrated that machine learning allows us to predict cognitive function in aged people using near-infrared spectroscopy (NIRS) data or basic blood test data. However, the following points are not yet clear: first, whether there are differences in prediction accuracy between NIRS and blood test data; second, whether there are differences in prediction accuracy for cognitive function in linear models and non-linear models; and third, whether there are changes in prediction accuracy when both NIRS and blood test data are added to the input layer. We used a linear regression model (LR) for the linear model and random forest (RF) and deep neural network (DNN) for the non-linear model. We studied 250 participants (mean age = 73.3 ± 12.6 years) and assessed cognitive function using the Mini Mental State Examination (MMSE) (mean MMSE scores = 22.9 ± 6.1). We used time-resolved NIRS (TNIRS) to measure absolute concentrations of hemoglobin and optical pathlength at rest in the bilateral prefrontal cortices. A basic blood test was performed on the same day. We compared predicted MMSE scores and grand truth MMSE scores; prediction accuracies were evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE). We found that (1) the DNN-based prediction using TNIRS data exhibited lower MAE and MAPE compared with those using blood test data, (2) the difference in MAPE between TNIRS and blood test data was only 0.3%, (3) adding TNIRS data to the blood test data of the input layer only improved MAPE by 1.0% compared to the use of blood test data alone, whereas the use of the blood test data alone exhibited the prediction accuracy with 81.8% sensitivity and 91.3% specificity (N = 202, repeated five-fold cross validation). Given these findings and the benefits of using blood test data (low cost and large-scale screening possible), we concluded that the DNN model using blood test data is still the most suitable for mass screening.

Highlights

  • According to the World Alzheimer Report, the population of Alzheimer’s disease (AD) patients is predicted to grow to over 100 million by 2050 [1]

  • In addition to age, the baseline concentrations of SO2 in the left and right prefrontal cortex (PFC) are useful for estimating the Mini Mental State Examination (MMSE) score using the time-resolved NIRS (TNIRS)

  • We found that prediction using TNIRS data exhibited lower mean absolute error (MAE) and mean absolute percentage error (MAPE) compared with those using blood test data, the difference in MAPE between TNIRS and blood test data was only 0.3%

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Summary

Introduction

According to the World Alzheimer Report, the population of Alzheimer’s disease (AD) patients is predicted to grow to over 100 million by 2050 [1]. At present, there are no drugs that can cure advance dementia, and delaying the onset of dementia has received great attention [2]. To this end, screening tests for cognitive dysfunction play a crucial role in prevention. ML-Based Assessment of Cognitive Impairment of dementia. The MMSE is sensitive and cost-effective screening test; it is a subjective examination, and does not allow clinicians to examine a large number of patients in a short time since it is carried out one by one between the clinicians and the patients. The MMSE is difficult to perform on patients with neurological disorders such as visual and hearing impairment

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