Unsupervised discovery of Mild Cognitive Impairment subtypes of Alzheimer's disease using consensus clustering and unsupervised learning techniques.
Fahimeh NezhadmoghadamJosé Gerardo Tamez-PeñaPublished in: ICBRA (2022)
Keyphrases
- mild cognitive impairment
- unsupervised learning
- consensus clustering
- early diagnosis
- high degree of accuracy
- clustering ensemble
- brain images
- clinical trials
- data clustering
- computer aided diagnosis
- breast cancer
- cluster ensemble
- early stage
- supervised learning
- k means
- computer aided
- early detection
- semi supervised
- clustering algorithm
- expectation maximization
- machine learning
- dimensionality reduction
- brain connectivity
- object recognition
- text classification
- combining multiple
- model selection
- gene expression profiles
- clustering quality
- cluster analysis
- medical images
- eeg data
- human brain
- data mining
- lung cancer
- knn
- magnetic resonance
- image data
- image analysis
- data analysis
- healthy subjects
- feature selection