ABSTRACT
Introduction: Dementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately. Methods: Quantitative research was conducted to address the objective of this study using randomly selected 343 Saudi patients. The Chi-square test was conducted to determine the association of the patient's cognitive function with various features, including demographical and medical history. Two widely used AI algorithms, logistic regression and support vector machine (SVM), were used for detecting cognitive decline. This study also assessed patients' cognitive function based on gender and developed the predicting models for males and females separately. Results: Fifty four percent of patients have normal cognitive function, 34% have MCI, and 12% have dementia. The prediction accuracies for all the developed models are greater than 71%, indicating good prediction capability. However, the developed SVM models performed the best, with an accuracy of 93.3% for all patients, 94.4% for males only, and 95.5% for females only. The top 10 significant predictors based on the developed SVM model are education, bedtime, taking pills for chronic pain, diabetes, stroke, gender, chronic pains, coronary artery diseases, and wake-up time. Conclusion: The results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia. This research can also stipulate substantial direction and supportive intuitions for scholars to enhance their understanding of crucial research, emerging trends, and new developments in future cognitive decline studies.
ABSTRACT
Purpose: Current evidence of whether napping promotes or declines cognitive functions among older adults is contradictory. The aim of this study was to determine the association between nap duration and cognitive functions among Saudi older adults. Methods: Old adults (> 60 years) were identified from the Covid-19 vaccine center at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia between May and August 2021. Face-to-face interviews were conducted by a geriatrician or family physicians. Data collected for each participant included sociodemographic, sleep patterns, health status and cognitive functions. St. Louis University mental status (SLUMS) was used to assess the cognitive functions. A multi-Linear regression model was used to determine the association between cognitive functions and nap duration. Results: Two-hundred participants (58 females) aged 66 ± 5 years were recruited. Participants were categorized according to their nap duration into non-nappers (0 min), short nappers (> 0- ≤ 30 min), moderate nappers (> 30-≤ 90 min), and extended nappers (> 90 min). The mean duration of the nap was 49.1 ± 58.4 min. The mean SLUMS score was 24.1 ± 4.7 units. Using the multi-linear regression model, the mean total SLUMS score for extended nappers was, on average, significantly lower than non-nappers [-2.16 units; 95% CI (-3.66, -0.66), p = < 0.01] after controlling for the covariates (age, sex, education level, sleep hours, diabetes mellitus, hypertension, pain). Conclusions: Extended napping was associated with deterioration in cognitive function among Saudi older adults.
