A new projection algorithm for variational inclusion problems and its application to cervical cancer disease prediction

Abstract

This study proposes a new projection algorithm for solving variational inclusion problems that exhibit weak convergence under suitable conditions in Hilbert space. Furthermore, we apply our algorithm to solve data classification using the cervical cancer behavior risk dataset. The comparison is done in terms of accuracy, precision, recall, and F1-score with other literature. Our proposed algorithm has proved to be more performant than other benchmark techniques for solving regularized least squares problems with different norms.