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Older Adults Get Lost in Virtual Reality: Visuospatial Disorder Detection in Dementia Using a Voting Approach Based on Machine Learning Algorithms

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Listed:
  • Areej Y. Bayahya

    (Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Department of Computer Science, Arab Open University, Jeddah 12015, Saudi Arabia)

  • Wadee Alhalabi

    (Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Virtual Reality Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Department of Computer Science, Dar Alhekma University, Jeddah 21589, Saudi Arabia)

  • Sultan H. Alamri

    (Department of Family Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Saudi Geriatrics Society, Riyadh 11614, Saudi Arabia
    Geriatrics Service, Fakeeh Care Group, Jeddah 23323, Saudi Arabia)

Abstract

As the age of an individual progresses, they are prone to more diseases; dementia is one of these age-related diseases. Regarding the detection of dementia, traditional cognitive testing is currently one of the most accurate tests. Nevertheless, it has many disadvantages, e.g., it does not measure the extent of the brain damage and does not take the patient’s intelligence into consideration. In addition, traditional assessment does not measure dementia under real-world conditions and in daily tasks. It is therefore advisable to investigate the newest, more powerful applications that combine cognitive techniques with computerized techniques. Virtual reality worlds are one example, and allow patients to immerse themselves in a controlled environment. This study created the Medical Visuospatial Dementia Test (referred to as the “MVD Test”) as a non-invasive, semi-immersive, and cognitive computerized test. It uses a 3D virtual environment platform based on medical tasks combined with AI algorithms. The objective is to evaluate two cognitive domains: visuospatial assessment and memory assessment. Using multiple machine learning algorithms (MLAs), based on different voting approaches, a 3D system classifies patients into three classes: patients with normal cognition, patients with mild cognitive impairment (MCI), and patients with severe cognitive impairment (dementia). The model with the highest performance was derived from voting approach named Ensemble Vote, where accuracy was 97.22%. Cross-validation accuracy of Extra Tree and Random Forest classifiers, which was greater than 99%, indicated a greater discriminate capacity than that of other classes.

Suggested Citation

  • Areej Y. Bayahya & Wadee Alhalabi & Sultan H. Alamri, 2022. "Older Adults Get Lost in Virtual Reality: Visuospatial Disorder Detection in Dementia Using a Voting Approach Based on Machine Learning Algorithms," Mathematics, MDPI, vol. 10(12), pages 1-25, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:1953-:d:832897
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    Cited by:

    1. Babek Erdebilli & Burcu Devrim-İçtenbaş, 2022. "Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey," Mathematics, MDPI, vol. 10(14), pages 1-16, July.

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