Mortality prediction in critically ill patients using machine learning score

Fatimah, Dzaharudin and Azrina, Md Ralib and Ummu Kulthum, Jamaludin and Mohd Basri, Mat Nor and Afidalina, Tumian and Har, Lim Chiew and Ceng, T. C. (2020) Mortality prediction in critically ill patients using machine learning score. In: IOP Conference Series: Materials Science and Engineering; 5th International Conference on Mechanical Engineering Research 2019, ICMER 2019 , 30-31 July 2019 , Kuantan. pp. 1-12., 788 (012029). ISSN 1757-8981

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Scoring tools are often used to predict patient severity of illness and mortality in intensive care units (ICU). Accurate prediction is important in the clinical setting to ensure efficient management of limited resources. However, studies have shown that the scoring tools currently in use are limited in predictive value. The aim of this study is to develop a machine learning (ML) based algorithm to improve the prediction of patient mortality for Malaysian ICU and evaluate the algorithm to determine whether it improves mortality prediction relative to the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment Score (SOFA) scores. Various types of classification algorithms in machine learning were investigated using common clinical variables extracted from patient records obtained from four major ICUs in Malaysia to predict mortality and assign patient mortality risk scores. The algorithm was validated with data obtained from a retrospective study on ICU patients in Malaysia. The performance was then assessed relative to prediction based on the SAPS II and SOFA scores by comparing the prediction accuracy, area under the curve (AUC) and sensitivity. It was found that the Decision Tree with SMOTE 500% with the inclusion of both SAPS II and SOFA score in the dataset could provide the highest confidence in categorizing patients into two outcomes: death and survival with a mean AUC of 0.9534 and a mean sensitivity 88.91%. The proposed ML score were found to have higher predictive power compared with ICU severity scores; SOFA and SAPS II.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Machine learning; Mortality prediction; Severity; SAPS II score; SOFA score
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculty/Division: Institute of Postgraduate Studies
Faculty of Mechanical and Automotive Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 06 Nov 2023 01:09
Last Modified: 06 Nov 2023 01:09
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