Dimensionality reduction is a technique used in data analysis and machine learning to reduce the number of input variables in a dataset. This process can help to simplify models, decrease computation time, and improve the performance of algorithms. It is particularly useful in the field of neonatal disorders, where high-dimensional data from various sources like genomic studies, medical imaging, and electronic health records are often analyzed.
