Assessment of the Awareness, Acceptance, and Willingness of Bahraini Public to Consume Genetically Modified Food

Hossain, S. M. Zakir; Sultana, Nahid; Elkanzi, E.M.; Habib, Maram; Ahmed, Noor Faris

doi:10.9734/bjast/2016/22873

Cited by 1 publication

(1 citation statement)

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“…The random survey was carried out from May 2020 to January 2021, and data were collected from 343 patients, which is an acceptable sample size for a statistical evaluation to characterize the parameters for observational studies with large population size ( Zakir Hossain et al, 2016 ; Düştegör et al, 2018 ; Sultana, 2019 ; Sultana, 2022 ). There were dropouts in the study.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence approaches for early detection of neurocognitive disorders among older adults

AlHarkan,

Sultana,

Al Mulhim

et al. 2024

Front. Comput. Neurosci.

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IntroductionDementia 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.MethodsQuantitative 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.ResultsFifty 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.ConclusionThe 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.

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Section: Methodsmentioning

confidence: 99%