Name
Title | ||
|---|---|---|
Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer's Disease Classification using Stacked Sparse Autoencoder. |
Abstract | ||
|---|---|---|
In recent years, the accurate detection of Alzheimer's disease (AD) at its early stage, using various biomarkers through machine learning techniques, has been given paramount importance in the medical field. However, in reality, the input datasets contain lots of missing values due to several factors such as increasing mortality rate, avoiding invasive procedures, and dropping out from the study. In this work, after analyzing the pattern of structural and clinical data from tadpole study in Alzheimer's disease neuroimaging initiative (ADNI) database, it has been found that the unobserved data are not missing completely at random. In view of this fact, with the assumption that the missing data patterns are in blocks, we propose a novel stacked sparse autoencoder based method to assign a value in the missing places and to select the significant structural and clinical features in order to discriminate the patients having AD, mild cognitive impairment (MCI), and cognitively normal (CN) clinical status. Through experimental results, it is shown that the proposed imputation algorithm achieves better performance for semi-supervised AD classification in terms of accuracy, sensitivity, and specificity in 5-fold cross validation when compared to the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIOCAS.2018.8584844 | 2018 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS): ADVANCED SYSTEMS FOR ENHANCING HUMAN HEALTH |
Keywords | Field | DocType |
Alzheimer's disease (AD),Structural magnetic resonance imaging (MRI),Neurophysiological test,Imputation,Feature selection,Stacked sparse autoencoder,Supervised classification | Disease classification,Computer vision,Autoencoder,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Imputation (statistics),Missing data,Neuroimaging,Cross-validation,Cognitive impairment | Conference |
ISSN | Citations | PageRank |
2163-4025 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emimal Jabason | 1 | 0 | 1.69 |
| M. O. Ahmad | 2 | 1157 | 154.87 |
| M. N. Swamy | 3 | 104 | 18.85 |