Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms

A smart healthcare-based system for classification of dementia using deep learning

Objectives

This study aims to develop a deep learning-based classification model for early detection of dementia using a wearable device that can measure the electrical conductivity of the skin, temperature, and movement as factors related to dementia, interlocking them with an application, and analyzing the collected data.

Methods

This study was conducted on 18 elderly individuals (5 males, 13 females) aged 65 years or older who consented to the study. The Korean Mini-Mental State Examination survey for cognitive function tests was conducted by well-trained researchers. The subjects were first grouped into high- or low-risk group for dementia based on their Korean Mini-Mental State Examination score. Data obtained by wearable devices of each subject were then used for the classification of the high- and low-risk groups of dementia through a smart healthcare-based system implementing a deep neural network with scaled principal component analysis. The correlation coefficients between the Korean Mini-Mental State Examination score and the featured data were also investigated.

Results

Our study showed that the proposed system using a deep neural network with scaled principal component analysis was effective in detecting individuals at high risk for dementia with up to 99% accuracy and which performance was better compared with commonly used classification algorithms. In addition, it was found that the electrical conductivity of skin had the closest correlation with the results of the Korean Mini-Mental State Examination score among data collected through wearable devices in this study.

Conclusions

Our proposed system can contribute to effective early detection of dementia for the elderly, using a non-invasive and easy-to-wear wearable device and classification algorithms with a simple cognitive function test. In the future, we intend to have more subjects participate in the experiment, to include more relevant variables in the wearable device, and to analyze the effectiveness of the smart healthcare-based dementia classification system over the long term.