Name
Title | ||
|---|---|---|
Deep Structural and Clinical Feature Learning for Semi-Supervised Multiclass Prediction of Alzheimer’s Disease |
Abstract | ||
|---|---|---|
Although there is no cure for Alzheimer's disease (AD), the early accurate prediction of clinical status plays a significant role in preventing, treating, and slowing down the progression of the disease. However, the absence of a single test and the complexity of AD create delays in diagnosis. In recent years, diagnosis of AD using different biomarkers through machine learning techniques has been the hottest research in the medical field. However, a common bottleneck of the diagnostic performance is overfitting due to having lot of irrelevant features in the training data. In view of this fact, we propose a novel classification framework which uses unsupervised autoencoder network to select the subset from given structural and clinical features by exploring the linear and nonlinear relationship among them followed by a supervised multinomial logistic layer to automatically identify the patients having AD, mild cognitive impairment (MCI), and cognitively normal (CN) clinical status. Through experimental results on Alzheimer's disease neuroimaging initiative (ADNI) database, it is shown that the proposed classification algorithm achieves better performance 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/mwscas.2018.8623946 | Midwest Symposium on Circuits and Systems Conference Proceedings |
Keywords | Field | DocType |
Alzheimer's disease (AD),Structural magnetic resonance imaging (MRI),Neurophysiological test,Unsupervised feature selection,Supervised classification,Deep learning | Bottleneck,Autoencoder,Computer science,Control engineering,Biomarker (medicine),Artificial intelligence,Deep learning,Overfitting,Neuroimaging,Cross-validation,Feature learning,Machine learning | Conference |
ISSN | Citations | PageRank |
1548-3746 | 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 |