RETRACTED: Deep multilayer and nonlinear Kernelized Lasso feature learning for healthcare in big data environment (Retracted Article)

In this modern era, healthcare industry is being metamorphosed by the progress in machine learning (ML). By utilizing vast big data, ML is now being pre-owned in healthcare to bestow comparatively better patient care and has emerged in enhanced business consequences. In this paper an effective processing framework called deep multilayer and non-linear Kernelized Lasso feature learning (DM-NKLFL) is introduced to powerfully cope with the data explosion in image processing field. Our work dedicates to provide a general framework for both simple linear and complex non-linear relationships. This in turn helps to handle the increase in image scale without affecting the performance. The proposed DM-NKLFL method includes two parts, i.e., stepwise regression nonlinear Kernelized Lasso (SR-NKL) feature selection and deep multilayer pattern learning (DMPL). Specifically, SR-NKL is aimed at processing non-linear features to minimize time and complexity involved during feature selection whereas the DMPL is proposed to deeply learn data driven features to determine the underlying patterns. The DM-NKLFL method over the traditional state-of-the-art methods are validated both in time efficiency and quality of results using the big biological data.