Ali Zainab H, Hassan Esraa, Elgamal Shimaa, El-Rashidy Nora
Department of Embedded Network Systems and Technology, Faculty of Artificial Intelligence, Kafrelsheikh University, El-Geish St, Kafrelsheikh, 33516, Egypt.
Department of Electronics and Computer Engineering, School of Engineering and Applied Sciences, Nile University, Giza, Egypt.
Sci Rep. 2025 Jul 16;15(1):25872. doi: 10.1038/s41598-025-06310-4.
Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data. However, these models often cannot provide early detection of affected brain regions. Alternatively, mental test scores such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) offer valuable insights into the likelihood of dementia and cognitive impairments. The main objective of this study is to introduce an innovative and dependable context-aware health monitoring system based on fog computing to measure mental impairment in the elderly population. The framework provides screening tests utilizing MMSE and MoCA to achieve accurate and real-time monitoring of cognitive function, allowing for early detection and treatment of mental disorders. To assess the effectiveness of our screening test, we evaluated a dataset comprising 450 subjects with Mild Cognitive Impairment (MCI) from Kaferelshikh University. The aggregated dataset is categorized into three classes: (1) 150 patients with MCI, (2) 150 subjects with subcortical diseases, Parkinson's Disease (PD), and (3) 150 subjects with cortical diseases, Alzheimer's Disease (AD). To accurately determine health risks, we employ an ensemble AdaBoost model, providing superior performance in accuracy, precision, recall, F-score, and Area Under the Curve (AUC). To validate the effectiveness of our Machine Learning (ML) model on unseen data, we evaluate an additional 18 subjects using the proposed scoring test, with six subjects from each class. The results indicate that our proposed ML model achieves an impressive accuracy of 0.93, outperforming the MoCA score (0.90) and MMSE score (0.83). Through our research, we demonstrate the potential of our context-aware fog computing approach in significantly enhancing early diagnosis of dementia, leveraging mental test scores as valuable indicators.
最近，痴呆症研究主要集中在利用磁共振成像（MRI）来开发处理和分析大脑数据的学习模型。然而，这些模型往往无法对受影响的脑区进行早期检测。相比之下，诸如简易精神状态检查表（MMSE）和蒙特利尔认知评估量表（MoCA）等心理测试分数能为痴呆症和认知障碍的可能性提供有价值的见解。本研究的主要目标是引入一种基于雾计算的创新且可靠的情境感知健康监测系统，以测量老年人群的精神损伤。该框架利用MMSE和MoCA提供筛查测试，以实现对认知功能的准确实时监测，从而能够早期发现和治疗精神障碍。为了评估我们筛查测试的有效性，我们评估了来自卡费雷尔希赫大学的一个包含450名轻度认知障碍（MCI）受试者的数据集。汇总后的数据集分为三类：（1）150名MCI患者，（2）150名患有皮质下疾病、帕金森病（PD）的受试者，以及（3）150名患有皮质疾病、阿尔茨海默病（AD）的受试者。为了准确确定健康风险，我们采用了集成AdaBoost模型，该模型在准确率、精确率、召回率、F值和曲线下面积（AUC）方面表现优异。为了验证我们的机器学习（ML）模型在未见数据上的有效性，我们使用所提出的评分测试对另外18名受试者进行了评估，每个类别有6名受试者。结果表明，我们提出的ML模型实现了令人印象深刻的0.93的准确率，优于MoCA分数（0.90）和MMSE分数（0.83）。通过我们的研究，我们证明了情境感知雾计算方法在利用心理测试分数作为有价值指标显著增强痴呆症早期诊断方面的潜力。
