Data Imputation of Brain MRI Features with Enhanced Multinomial Logistic Regression for Alzheimer's Disease Classification
Author:
Affiliation:
1. G.T.N.Arts College, India
2. The Gandhigram Rural Institute - (Deemed to be University), India
3. The Gandhigram Rural Institute (Deemed to be University), India
4. Nagasaki University, Japan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3479645.3479676
Reference23 articles.
1. Impact of imputation of missing values on classification error for discrete data
2. Fabio Previtali and Paola Bertolazzi and Giovanni Felici and Emanuel Weitschek. 2017. A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis. Computer methods and programs in biomedicine 143:89-95. Fabio Previtali and Paola Bertolazzi and Giovanni Felici and Emanuel Weitschek. 2017. A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis. Computer methods and programs in biomedicine 143:89-95.
3. Independent component analysis-based classification of Alzheimer's disease MRI data;Wenlu Yang F.;Journal of Alzheimer's disease. 24(4);75-83.,2011
4. Cai K Xu H Guan H Zhu W Jiang J Cui Y 2017. Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices. PLoS ONE 12(1). Cai K Xu H Guan H Zhu W Jiang J Cui Y 2017. Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices. PLoS ONE 12(1).
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