College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Department of Computer Science, Kettering University, Flint, Michigan 48504, USA.
Comput Intell Neurosci. 2022 Aug 23;2022:5476714. doi: 10.1155/2022/5476714. eCollection 2022.
Alzheimer's Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.
阿尔茨海默病（AD）是一种无声的疾病，它会导致脑细胞逐渐死亡，影响意识、行为、规划能力和语言等方面。AD 随着年龄的增长呈指数级增长，每 5-6 年翻一番，导致严重后果，如吞咽困难和在死亡前丧失说话能力。根据沙特阿拉伯卫生部的数据，到 2060 年，AD 患者将增加两倍，达到全球 1400 万例。患者数量的快速增长是由于疾病的无声进展导致的，这导致了诊断延迟，因为症状无法与正常衰老影响区分开来。此外，由于目前的医疗能力，通过特定的医学检查无法 100%确定 AD。文献综述显示，大多数最近的出版物都使用图像来诊断 AD，但对于成像能力有限的当地医院来说，这还不够。其他使用临床和人口统计学数据的研究也没有取得足够的结果。因此，本研究旨在通过机器学习（ML）技术提前预测沙特阿拉伯的 AD。该数据集来自沙特阿拉伯达曼的法赫德国王专科医院（KFSH），包含 152 名患者的标准临床测试。使用了四种 ML 算法，即支持向量机（SVM）、k-最近邻（k-NN）、自适应增强（AdaBoost）和极端梯度提升（XGBoost），来提前诊断疾病。实证结果表明，SVM 在 AD 的提前诊断中具有鲁棒性，其准确性、精确性、召回率和接收者操作特征曲线下的面积（AUROC）分别为 95.56%、94.70%、97.78%和 0.97，在应用顺序前向特征选择技术后，有 13 个特征。该模型可以帮助医务人员以低成本控制疾病的进展。
