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


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.

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We thank the curators of NACC database for providing the data for our research work.

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Correspondence to N. Vinutha.

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Conflict of interest: All the authors declare no funding received from any source and there is no conflict of interest.

Ethical standards: This work was conducted in accordance with the guidelines provided by the Bangalore University.

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Vinutha, N., Pattar, S., Sharma, S. et al. A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer’s Disease: Data Preprocessing and Analysis. J Prev Alzheimers Dis (2020). https://doi.org/10.14283/jpad.2020.7

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Key words

  • Alzheimer’s disease
  • functional activities questionnaire
  • genetic algorithm
  • logistic regression
  • imputation
  • missforest
  • neuropsychological scores
  • synthetic minority oversampling technique