How is Unsupervised Learning Applied to Neonatal Disorders?
In the context of neonatal disorders, unsupervised learning can be invaluable for several reasons:
Clustering: Clustering algorithms can group similar cases of neonatal disorders, which can help in identifying subtypes of disorders and understanding their underlying mechanisms. Anomaly Detection: These algorithms can detect outliers in neonatal health data, potentially identifying rare conditions or early signs of developing disorders. Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) can reduce the complexity of neonatal health data, making it easier to visualize and interpret.
