Hepatitis C Virus positivity prediction from serum samples using NIRS and L1-penalized classification.
Óscar Barquero-PérezJosé Gómez-SánchezDaniel Riado-MínguezJennifer Gonzálo-SegoviaRafael García-CarreteroMaría Luisa Casas-LosadaS. Fernández-RodríguezMaría Luisa Gutiérrez-GarcíaElena Jaime-LaraEnrique Pérez-MartínezJavier Ramos-LópezSergio Salgüero-FernándezConrado Fernández-RodríguezMyriam CataláPublished in: EMBC (2022)
Keyphrases
- training samples
- classification accuracy
- training set
- prediction accuracy
- hepatitis c virus
- feature vectors
- support vector
- support vector machine svm
- pattern recognition
- decision trees
- classification scheme
- image classification
- text classification
- model selection
- machine learning
- class labels
- pattern classification
- classification rules
- neural networks and support vector machines
- training dataset
- automatic classification
- prediction model
- training data
- classification algorithm
- data sets
- machine learning algorithms
- least squares
- feature extraction
- classification models
- feature selection
- multi layer perceptron
- nearest neighbour
- ensemble classifier
- small sample
- optimum path forest
- biomarker discovery
- nearest neighbour algorithm
- neural network