Abstract: A system includes: one or more processing circuits including one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to: identify minority class data and majority class data in patient-level data, the minority class data corresponding to patients with a health failure, the majority class data corresponding to patients without the health failure; oversample the minority class data to obtain synthetic class data; automatically reduce, using a machine learning classifier, risk factor variables to a reduced set of risk factor variables based on the majority class data, the minority class data, and the synthetic class data; and provide one or more graphical user interfaces for display.

Abstract: Systems, apparatuses and methods may provide technology that identifies minority class data and majority class data in patient-level data, wherein the minority class data corresponds to patients with a health failure and the majority class data corresponds to patients without the health failure, oversamples the minority class data to obtain synthetic class data and automatically reduces, via a machine learning classifier, a set of risk factor variables based on the majority class data, the minority class data and the synthetic class data.

Abstract: Systems, apparatuses and methods may provide technology that identifies minority class data and majority class data in patient-level data, wherein the minority class data corresponds to patients with a health failure and the majority class data corresponds to patients without the health failure, oversamples the minority class data to obtain synthetic class data and automatically reduces, via a machine learning classifier, a set of risk factor variables based on the majority class data, the minority class data and the synthetic class data.