Why is Dimensionality Reduction Important in Epidemiology?
In epidemiology, datasets often contain a large number of variables, such as demographic information, clinical measurements, and genetic data. High-dimensional data can lead to several issues, including overfitting, increased computational burden, and difficulties in data visualization. Dimensionality reduction helps to mitigate these issues, enabling researchers to focus on the most significant factors influencing health outcomes.
