Low-rank and sparse representation based learning for cancer survivability prediction

•Cancer survivability prediction is a significant problem to health professionals.•A novel classification algorithm is proposed using low-rank and sparse representation.•Low-rank alternative of raw inputs is trained using a sparsity-enhanced classifier.•Experiments show superior performance compared to state-of-the-art approaches.