Sparsity is crucial because it can impact the accuracy and efficiency of computational models. Sparse datasets require different approaches compared to dense datasets, as traditional methods may not perform well. Understanding and managing sparsity allows researchers to develop more robust models for diagnosis, prognosis, and treatment planning.
