Uddin Khandaker Mohammad Mohi, Alam Mir Jafikul, Uddin Md Ashraf, Aryal Sunil
Department of Computer Science and Engineering, Dhaka International University, Dhaka, 1205 Bangladesh.
School of Information and Technology, Deakin University, Warun Ponds, Geelong, Australia.
Biomed Mater Devices. 2023 Apr 10:1-17. doi: 10.1007/s44174-023-00078-9.
Alzheimer's disease (AD) is one of the leading causes of dementia among older people. In addition, a considerable portion of the world's population suffers from metabolic problems, such as Alzheimer's disease and diabetes. Alzheimer's disease affects the brain in a degenerative manner. As the elderly population grows, this illness can cause more people to become inactive by impairing their memory and physical functionality. This might impact their family members and the financial, economic, and social spheres. Researchers have recently investigated different machine learning and deep learning approaches to detect such diseases at an earlier stage. Early diagnosis and treatment of AD help patients to recover from it successfully and with the least harm. This paper proposes a machine learning model that comprises GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost to predict Alzheimer's disease. The model is trained using the open access series of imaging studies (OASIS) dataset to evaluate the performance in terms of accuracy, precision, recall, and F1 score. Our findings showed that the voting classifier attained the highest validation accuracy of 96% for the AD dataset. Therefore, ML algorithms have the potential to drastically lower Alzheimer's disease annual mortality rates through accurate detection.
阿尔茨海默病（AD）是老年人痴呆症的主要病因之一。此外，世界上相当一部分人口患有代谢问题，如阿尔茨海默病和糖尿病。阿尔茨海默病以退行性方式影响大脑。随着老年人口的增长，这种疾病会通过损害他们的记忆和身体功能导致更多人变得活动不便。这可能会影响他们的家庭成员以及金融、经济和社会领域。研究人员最近研究了不同的机器学习和深度学习方法，以便在更早阶段检测此类疾病。阿尔茨海默病的早期诊断和治疗有助于患者以最小的伤害成功康复。本文提出了一种机器学习模型，该模型包括高斯朴素贝叶斯（GaussianNB）、决策树、随机森林、极端梯度提升（XGBoost）、投票分类器和梯度提升（GradientBoost），用于预测阿尔茨海默病。该模型使用开放获取影像研究系列（OASIS）数据集进行训练，以评估其在准确性、精确率、召回率和F1分数方面的性能。我们的研究结果表明，投票分类器在AD数据集上获得了96%的最高验证准确率。因此，机器学习算法有潜力通过准确检测大幅降低阿尔茨海默病的年死亡率。
