Identifying Key Factors Associated with Assistive Technology Availability for Dementia Care Using Machine Learning

Area of Science:

• Gerontology and Rehabilitation Technology
• Artificial Intelligence in Healthcare
• Public Health Policy

Background:

• Assistive technology (AT) is vital for maintaining independence and quality of life for individuals with dementia.
• The availability of AT for dementia care is influenced by a complex interplay of social, economic, and policy factors.
• Understanding these determinants is critical for developing effective support systems.

Purpose of the Study:

• To identify and analyze the key factors influencing the availability of assistive technology for people with dementia.
• To apply machine learning techniques to uncover patterns in AT accessibility data.
• To provide evidence-based recommendations for improving AT provision in dementia care.

Main Methods:

• Utilized machine learning, specifically a random forest model, for predictive analysis.
• Analyzed data encompassing carer support, legislative frameworks, and access to WHO Global Health Observatory (GHO) reimbursable products.
• Validated model performance through accuracy metrics.

Main Results:

• Carer support emerged as a significant predictor of AT availability.
• Protective legislation and access to WHO GHO-listed reimbursable products were identified as crucial factors.
• The random forest model demonstrated high accuracy in predicting AT accessibility.

Conclusions:

• Targeted resource allocation and policy interventions are essential for enhancing AT availability for dementia patients.
• Strengthening carer support systems can improve access to necessary assistive technologies.
• Policy development should consider legislative protections and integration with global health initiatives for better dementia care infrastructure.