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Modelling mobile-based technology adoption among people with dementia.

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Summary
This summary is machine-generated.

This study enhances technology adoption prediction by combining psychosocial and medical data, achieving 99.41% accuracy with k-Nearest Neighbors (kNN). Psychosocial factors are key, but integrated reliable medical data yields optimal results.

Keywords:
Assistive technologiesDementiaMedical historyReminder applicationTechnology adoption

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Area of Science:

  • Health Informatics
  • Behavioral Science
  • Computer Science

Background:

  • Previous research focused on technology adoption modeling using psychosocial and self-reported health data.
  • Current work integrates two datasets with detailed background, psychosocial, and medical history.
  • Enhancing understanding of technology adoption requires identifying optimal feature subsets.

Purpose of the Study:

  • To identify the best features for understanding technology adoption.
  • To develop and compare classification models for predicting technology adoption.
  • To improve prediction accuracy compared to previous studies.

Main Methods:

  • Feature selection was performed on combined psychosocial and medical history data.
  • Analysis and fusion of two detailed datasets were conducted.
  • k-Nearest Neighbors (kNN) and Neural Network (NN) algorithms were employed for adoption modeling.

Main Results:

  • The kNN algorithm achieved a prediction accuracy of 99.41% using combined psychosocial and medical data.
  • The NN algorithm achieved 94.08% accuracy.
  • Both models significantly improved upon the previous best accuracy of 92.48%.

Conclusions:

  • Psychosocial data is more predictive of technology adoption than medical data alone.
  • Combining psychosocial and reliable medical data provides the highest prediction accuracy.
  • Future technology adoption models should prioritize integrated, reliable medical information.