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Cognitive Impairment Prediction Model Using AutoML and Lifelog

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(11), pp.53-63
  • DOI : 10.9708/jksci.2023.28.11.053
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 26, 2023
  • Accepted : October 30, 2023
  • Published : November 30, 2023

Choi, Hyunchul 1 Chiho Yoon 2 Sae Bom Lee 3

1경희대학교
2지방공기업평가원
3삼육대학교

Accredited

ABSTRACT

This study developed a cognitive impairment predictive model as one of the screening tests for preventing dementia in the elderly by using Automated Machine Learning(AutoML). We used ‘Wearable lifelog data for high-risk dementia patients’ of National Information Society Agency, then conducted using PyCaret 3.0.0 in the Google Colaboratory environment. This study analysis steps are as follows; first, selecting five models demonstrating excellent classification performance for the model development and lifelog data analysis. Next, using ensemble learning to integrate these models and assess their performance. It was found that Voting Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting, Light Gradient Boosting Machine, Extra Trees Classifier, and Random Forest Classifier model showed high predictive performance in that order. This study findings, furthermore, emphasized on the the crucial importance of 'Average respiration per minute during sleep' and 'Average heart rate per minute during sleep' as the most critical feature variables for accurate predictions. Finally, these study results suggest that consideration of the possibility of using machine learning and lifelog as a means to more effectively manage and prevent cognitive impairment in the elderly.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.