Technology to automatically identify the most relevant health failure risk factors

At least one computer readable storage medium comprising a set of instructions, which when executed by a computing system, cause the computing system to:

• identify 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;
• oversample the minority class data to obtain synthetic class data;
• and
• automatically reduce, 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.

A method comprising:

• identifying 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;
• oversampling the minority class data to obtain synthetic class data;
• and
• automatically reducing, 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.