Diagnosis of Sepsis Using Ratio Based Features

Abstract

Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to use machine learning for early prediction of sepsis using ratio and power-based feature transformation. The feature transformation and feature selection process is optimized by applying a genetic algorithm (GA) based approach to extract the information specific to the sepsis from the given raw patient covariates that maximizes the underlying classification performance in terms of utility score. The proposed method begins with filling the missing values in the training dataset. Then, GA is applied strategically to identify influential ratio and power-based features from the raw patient covariates. The utility score is maximized as an objective of the optimization. RusBoost is used with default settings for underlying classification during optimization. Subsequently, an optimal RusBoost model is developed with a set of 55 identified features. Independent performance evaluation of the proposed method with the 2019 PhysioNet/CinC Challenge dataset has officially achieved 19th rank with a utility score of 30.9% on the full hidden test data. This work appears as Shivpatidar on the leader-board. The proposed early warning system has potential clinical value in critical care clinics.