Correlation based Feature Selection using Rank aggregation for an Improved Prediction of Potentially Preventable Events

Abstract

This paper presents a methodology for developing a novel feature selection model that will help in a more accurate and robust prediction of patients with the risk of Potentially Preventable Events (PPEs). PPEs are admissions, readmissions, complications and emergency department visits that could have been avoided if the patient had been given the appropriate interventions. Various clinical factors and patient health conditions can affect a patient's chance of developing the risk of PPE. We propose a robust Correlation based feature selection method using Rank Aggregation (CRA) which helps to identify the key contributing factors for the prediction of PPE. Unlike existing feature selection techniques that causes bias by using distinct statistical properties of data for feature evaluation, CRA uses rank aggregation thus reducing this bias. The result indicates that the proposed technique is more robust across a wide range of classifiers and has higher accuracy than other traditional methods.