Feature extraction can be performed using a variety of techniques, depending on the type of data:
Genomic Data: Techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are employed to reduce the dimensionality of data and highlight significant genetic variations. Imaging Data: Methods like Convolutional Neural Networks (CNNs) are utilized to extract features from medical images, identifying abnormalities that may indicate cancer. Clinical Data: Statistical techniques and natural language processing (NLP) are used to extract features from electronic health records (EHRs).
