Computer Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey.
Computer Engineering, Eskisehir Technical University, Eskisehir, Turkey.
PeerJ. 2022 May 26;10:e13425. doi: 10.7717/peerj.13425. eCollection 2022.
Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect. In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine. This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner. Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors. By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores. The complete base of 816 ADNI1-screening samples was processed, and a hybrid set of 603 features was handled. Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis. Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques. The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.
医生在诊断痴呆症时可能会遇到困难，特别是在临床测试分数缺失或不正确的情况下。在有疑问的情况下，在医学中检查痴呆症时，形态计量学和人口统计学都至关重要。本研究旨在通过脑 MRI 分析和其他人口统计学信息来推断和验证临床测试分数，从而提出一种易于理解的决策支持系统，以改善诊断和预后。因此，我们通过无监督的基于用户的与痴呆症相关的协作过滤来推断缺失的临床测试分数值，以最大程度地减少错误。通过分析继承率，我们提出了一种可靠性量表，可用于现有临床测试分数的一致性。处理了完整的 816 个 ADNI1 筛查样本基础，并处理了 603 个特征的混合集。此外，还评估了使用的详细参数，例如最佳邻域和输入特征，以进行进一步的比较分析。总体而言，某些协作过滤配置的表现优于替代的最先进的插补技术。基于所提出的方法的插补系统和可靠性量表有望支持临床测试。
