Feature selection using generalized linear model for Machine Learning-based sepsis prediction

Mohammed Ashikur, Rahman and Abubakar, Adamu and Tumian, Afidalina (2023) Feature selection using generalized linear model for Machine Learning-based sepsis prediction. In: International Conference on Advances in Intelligent Computing and Applications (AICAPS), 1st to 3rd February 2023, India.

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Abstract

Sepsis is a life-threatening condition of patients in an intensive care unit. Early sepsis detection can reduce the mortality rate and cost of treatment among the patients of the Intensive care unit (ICU). Machine Learning-based model can be used to predict sepsis early using Electronic Health Record (EHR) which consists of big data. Features selection plays a vital role for reducing overfitting and the accuracy of the MLbased prediction model. In this paper, Generalized Linear Model (GLM) was used to select the significant features related to sepsis using MIMIC-III dataset which is a rational database that contains ICU patient’s data at Beth Israel Deaconess Medical center. In addition, developed a sepsis prediction model using Artificial Neural Network (ANN) and Random Forest (RF) and validated those models using confusion matrix. After that, clinical severity scores were also calculated with the same dataset. Finally, compared the Area Under the Receiver Operating Characteristic (AUROC) between MLbased model and clinical severity score. The accuracy of MLbased prediction model with GLM is better than clinical severity scores like SOFA, qSOFA and SIRS

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	Sepsis prediction, Machine Learning, Feature selection, Generalized linear model
Subjects:	Q Science > Q Science (General) > Q300 Cybernetics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Adamu Abubakar
Date Deposited:	11 Sep 2023 09:35
Last Modified:	23 Jan 2024 16:42
URI:	http://irep.iium.edu.my/id/eprint/106530

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