Artificial Intelligence in Elder Care: Predicting Health Risks and Social Needs of Nigeria’s Aging Population

Abstract

The study investigates the role of artificial intelligence (AI) in predicting health risks and social needs among Nigeria’s aging population. As the demographic of the country’s elderly population continues to expand at a rapid pace. There are increased demand and reliance on the health care system, heath care professional continuously faces an increasing pressure to provide effective, timely, and affordable care. This research adopts a mixed-method design that combines quantitative and qualitative approaches to assess the predictive capacity and integration of AI technologies in the revolutionization of healthcare services that is beneficial to the elderly thereby enhancing their quality of life through assistive technologies, and social engagement for the elderly, offering solutions that promise increased independence, improved health management, and reduced social isolation. anchored in the recognition of the unique potential that AI holds for enhancing the lives of seniors, while also acknowledging the practical challenges that accompany its adoption in elder care ensuring that AI adoption in elder care is aligned with the real needs and preferences of the elderly population. By presenting current state-of-the-art developments, potential benefits in elder care. Primary data were collected from 300 elderly participants, 30 healthcare practitioners, and 10 artificial intelligence system developers across Lagos, Enugu, and Kaduna States in Nigeria. Descriptive statistics, regression analysis, machine learning algorithms logistic regression, random forest, and support vector machines were employed to model and predict health risks, while thematic analysis was used for qualitative data interpretation. The findings revealed that AI-based predictive models, particularly the random forest algorithm, achieved the highest accuracy (87.5%) in forecasting chronic health conditions such as hypertension, diabetes, and cardiovascular diseases. A strong positive correlation (r = 0.81) was found between artificial intelligence predictions and actual health outcomes, demonstrating the reliability of AI such as smart homes with including voice-activated assistant for elderly, AI- based health monitoring technologies, non-invasive wearable and portable hand held , short distance censored devices for measuring physiological signs such as electrocardiogram , Spirometers for asthma, chronic obstructive pulmonary disease , and other respiratory disorders, electromyogram , Haemoglobin level detectors, heart rate (HR), body temperature, electrodermal activity , Pulse Oximeters, arterial oxygen saturation (SpO2), blood pressure (BP) and respiration rate (RR) , interactive and communicative AI driven gamification for cognitive training and brain games designed to stimulate mental functions and improve cognitive health, reminders such as AI companions for medication reminders, appointments scheduling and daily conversations to reduce the feeling of isolation and improve overall mental well-being. Furthermore, There are some technologies for physical therapy in elderly care, Robotic systems to monitor elderly individuals remotely while performing their routine activities can significantly support the enhancement of smart healthcare services for the elderly community, support the identification of life threatening triggers and changes for vulnerable elderly individuals and can alert caregivers if deviations occur, thereby enhancing safety and enabling the elderly population to live independently for longer periods. Qualitative results indicated that while healthcare providers recognize the preventive benefits of smart systems, challenges such as low digital literacy, ethical concerns, data privacy, lack of human-centric care in the AI-augmented landscape, lack of electronic health record data governance laws and policies and inadequate infrastructure hinder large-scale adoption. The study concludes that AI intelligent agents offer significant potential in improving preventive health management, social care coordination, and overall well-being of older adults in Nigeria. It recommends integrating cognitive computing, embedded clinical workflows, health technologies into the national healthcare policy, strengthening digital literacy among caregivers and elderly populations, and establishing ethical data governance frameworks to ensure transparency and inclusivity in Nigeria.

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