Focal Loss Improves Performance of High-Sensitivity C-Reactive Protein Imbalanced Classification

Abstract

Chronic inflammation has been shown to be associated with cardiovascular disorders, atherosclerosis, and colorectal adenoma. Using a high-sensitivity assay, we can detect levels of High-Sensitivity C-Reactive Protein (HSCRP), which in turn, yields an understanding of systemic low-grade chronic inflammation. Prediction of HSCRP has historically been performed to determine association with other factors that impact its prediction. To our knowledge, it is generally not performed for prediction itself. Here, we utilize Focal Loss Logistic Regression, a variation of log-loss Logistic Regression to achieve increased performance of HSCRP classification. With the use of this model, one can perform imputation of HSCRP in the case of missing data. It also can be utilized for medical professionals as a screen to determine if an HSCRP test should be performed.