A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer's Disease: Data Preprocessing and Analysis.

用于评估阿尔茨海默病认知和功能损害的机器学习框架 ： 数据预处理和分析。

• 影响指数: 5.02
• 发表时间: 2020
• 来源期刊: J Prev Alzheimers Dis
• DOI: 10.14283/jpad.2020.7
• PMID: 32236397
• 文章类型: Journal Article
• 作者列表: Vinutha N,Pattar S,Sharma S,Shenoy PD,Venugopal KR

关键词: Alzheimer’s disease; functional activities questionnaire; genetic algorithm; imputation; logistic regression; missforest; neuropsychological scores; synthetic minority oversampling technique

Mesh: Activities of Daily Living; Alzheimer Disease / diagnosis; Cognitive Dysfunction / diagnosis; Datasets as Topic; Executive Function; Humans; Logistic Models; Machine Learning; Memory; Mental Status and Dementia Tests

Abstract

The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer\'s Disease. Further, there are standardized dataset available today that are curated from several centers across the globe that aid in development of Computer Aided Diagnosis tools. However, there are numerous clinical tests to measure these scores that lead to a challenging task for their assessment in diagnosis. Also, the datasets suffer from common missing and imbalanced data issues. In this paper, we propose a machine learning based framework to overcome these issues. Empirical results demonstrate that improved performance of Genetic Algorithm is obtained for the neuropsychological scores after Miss Forest Imputation and for FAQ scores is obtained after subjecting it to the Synthetic Minority Oversampling Technique.

摘要

神经心理学评分和功能活动问卷(FAQ)对测量阿尔茨海默病患者的认知和功能域具有重要意义。Further,今天有标准化的数据集，这些数据集来自全球多个中心，有助于开发计算机辅助诊断工具。然而,有许多临床测试来衡量这些分数，这导致了一项具有挑战性的任务，以评估他们的诊断。此外，数据集存在常见的数据缺失和不平衡问题。在本文中,我们提出了一个基于机器学习的框架来克服这些问题。经验结果表明，遗传算法在MissForest填充后的神经心理学得分和FAQ得分上的性能得到了改善。