A data scientist at a healthcare analytics company is facing challenges in developing a predictive model for patient outcomes using a dataset with hundreds of features, many of which are correlated. The high dimensionality and resulting sparsity of the data are hindering the model's ability to converge effectively. What preprocessing strategy should be employed to mitigate the "curse of dimensionality" and enhance the model's convergence on meaningful predictions?