Anomaly Detection Using Principal Component Analysis (PCA) The main advantage of using PCA for anomaly detection, compared to alternative techniques such as a neural autoencoder, is simplicity -- ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

AI-enabled prediction of PCa using urine-based liquid biopsy demonstrates accurate, rapid, and accessible early cancer detection, with consistent performance across disease grades. This non-invasive ...

PURPOSEProstate cancer (PCa) is among the leading causes of cancer deaths. While localized PCa has a 5-year survival rate approaching 100%, this rate drops to 31% for metastatic prostate cancer (mPCa) ...