Sano Yuko, Suzumura Shota, Sugioka Junpei, Mizuguchi Tomohiko, Kandori Akihiko, Kondo Izumi
Center for Digital Services, Healthcare Innovation, Research and Development Group, Hitachi, Ltd., Kokubunji, Japan.
Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan.
Med Biol Eng Comput. 2025 Jun;63(6):1881-1894. doi: 10.1007/s11517-025-03306-0. Epub 2025 Feb 1.
Early detection of dementia is essential to reduce the decline in quality of life (QoL) and the increase in medical and nursing care costs associated with dementia in an aging society. In this study, we aimed to develop a simple screening test for mild cognitive impairment (MCI), a preliminary stage of dementia, by creating an analytical method to accurately detect MCI through finger-tapping measurement. We extracted 248 characteristics from the finger-tapping waveforms of 182 MCI patients and 352 normal controls, applying five conventional classification methods along with an improved Random Forest (RF) method proposed in this study (Integrated RF). In the proposed method, the RF classification model for the MCI and normal control groups is supplementally integrated with the RF classification model for the Alzheimer's disease and normal control groups to generate a new classification model. When comparing the discrimination accuracy of each method, the proposed method achieved the highest accuracy, with an F1-score of 0.795 (recall = 0.778 and precision = 0.814). These results demonstrate the potential of finger-tapping measurement as a highly accurate screening test for MCI.
在老龄化社会中，早期发现痴呆症对于降低生活质量（QoL）的下降以及与痴呆症相关的医疗和护理成本的增加至关重要。在本研究中，我们旨在通过创建一种通过手指敲击测量准确检测轻度认知障碍（MCI）（痴呆症的初步阶段）的分析方法，开发一种简单的MCI筛查测试。我们从182名MCI患者和352名正常对照的手指敲击波形中提取了248个特征，应用了五种传统分类方法以及本研究中提出的改进随机森林（RF）方法（集成RF）。在所提出的方法中，MCI组和正常对照组的RF分类模型与阿尔茨海默病组和正常对照组的RF分类模型补充集成，以生成新的分类模型。在比较每种方法的判别准确性时，所提出的方法达到了最高准确性，F1分数为0.795（召回率 = 0.778，精确率 = 0.814）。这些结果证明了手指敲击测量作为MCI高度准确筛查测试的潜力。
