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Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks.

Antti Etholén1, Teemu Roos2, Mirja Hänninen1,3

  • 1Department of Public Health, University of Helsinki, Helsinki, Finland.

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Summary
This summary is machine-generated.

This study used artificial intelligence to develop a risk score for subjective cognitive decline (SCD), identifying physical activity as a key protective factor. The tool aids personalized prevention strategies to reduce dementia risk.

Keywords:
AIBMIagingalcohol consumptionartificial intelligencebody mass indexconsumption of fruit and vegetablesdementiainsomnia symptomsleisure time physical activitysmoking

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Area of Science:

  • Neuroscience and Public Health
  • Utilizing artificial intelligence (AI) for predictive modeling in cognitive health.

Background:

  • Subjective cognitive decline (SCD) is linked to modifiable risk factors.
  • Previous forecasting models for SCD have not incorporated developmental risk factor patterns.
  • There is a need for practical tools to prevent cognitive decline.

Purpose of the Study:

  • To examine multifactorial trajectories of risk factors and their association with SCD using an AI approach.
  • To develop a score calculator for forecasting later SCD.
  • To create a risk score tool for personalized risk assessment and intervention planning, validated against dementia diagnoses.

Main Methods:

  • Employed dynamic Bayesian networks trained on five repeated surveys (2000-2022) from the Helsinki Health Study (N=8960).
  • Predicted SCD (memory, learning, concentration) based on lifestyle factors (diet, smoking, alcohol, physical activity, BMI, insomnia), adjusting for covariates.
  • Assessed model performance using 5-fold cross-validation and receiver operating characteristic (ROC) curve analysis.

Main Results:

  • Physical activity was the strongest predictor of SCD (OR 0.76).
  • Alcohol consumption exhibited a U-shaped relationship with SCD.
  • SCD predicted dementia diagnoses, with memory decline increasing dementia likelihood over 3-5 times by 2022 (AUC 0.75-0.78).

Conclusions:

  • Developed an AI-driven risk score tool for personalized SCD risk assessment and intervention.
  • The tool can assist healthcare professionals in guiding lifestyle interventions to mitigate cognitive decline and dementia risk.
  • Early identification of SCD through AI models can enhance targeted dementia prevention strategies.