Novel Feature Selection for Artificial Intelligence Using Item Response Theory for Mortality Prediction | IEEE Conference Publication | IEEE Xplore

Novel Feature Selection for Artificial Intelligence Using Item Response Theory for Mortality Prediction


Abstract:

Feature selection is a critical component in supervised machine learning classification analyses. Extraneous features introduce noise and inefficiencies into the system l...Show More

Abstract:

Feature selection is a critical component in supervised machine learning classification analyses. Extraneous features introduce noise and inefficiencies into the system leading to a need for feature reduction techniques. Many feature reduction models use the end-classification results in the feature reduction process, committing a circular error. Item Response Theory (IRT) examines the characteristics of features independent of the end-classification results, and provides high levels of information regarding feature utility. A two-parameter dichotomous IRT model was used to analyze 18 features from an intensive care unit data set with 2520 cases. The classification results showed that the features selected via IRT were comparable to that using more traditional machine learning approaches. Strengths and limitations of the IRT selection protocol are discussed.
Date of Conference: 20-24 July 2020
Date Added to IEEE Xplore: 27 August 2020
ISBN Information:

ISSN Information:

PubMed ID: 33019275
Conference Location: Montreal, QC, Canada

I. INTRODUCTION

Health-related data sets often contain many features relative to the number of cases, giving rise to the problem of how to select a few relevant features and discard irrelevant ones, before subjecting the data to machine learning protocols to increase predictive accuracy [1]. This ‘curse of dimensionality’ has been the subject of much debate in the machine learning literature [2], [3]. Current supervised machine learning programs incorporate a feature selection tool as part of the process. However, they use the existing classification outcome information within the selection process introducing bias into the process and result in models that may overfit the data and may not generalize [4]. An approach to feature selection that avoids the biasing problem, as the analysis operates independently of the classification outcomes, uses Item Response Theory (IRT). IRT has been applied in machine learning contexts, such as comparisons of collaborative filtering [5], natural language processing [6], identification of initial computer adaptive learning items [7], and classifier assessment [8], suggesting is has the potential to address some long-standing concerns of this research community. The purpose of the current study, however, is to demonstrate the use of IRT in feature selection using intensive care data and no-death/death outcomes for patients.

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