Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit
Multiple organ failures are the main cause of mortality and morbidity in the intensive care unit (ICU). The progression of organ failures in the ICU is usually monitored using the Sequential Organ Failure Assessment (SOFA) score. This study aims to perform the classification of multiple organ failur...
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my.uniten.dspace-370402025-03-03T15:46:52Z Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit Shah N.N.H. Razak N.N.A. Razak A.A. Abu-Samah A. Suhaimi F.M. Jamaluddin U. 7401823793 37059587300 56960052400 56719596600 36247893200 55330889600 Multiple organ failures are the main cause of mortality and morbidity in the intensive care unit (ICU). The progression of organ failures in the ICU is usually monitored using the Sequential Organ Failure Assessment (SOFA) score. This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. Ninety-eight ICU patients? data were obtained retrospectively from Universiti Malaya Medical Centre for analysis. Several machine learning algorithms which are decision tree, linear discriminant, na�ve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. The classifiers were trained on 80% of the patients with 10-fold cross-validations and assessed on 20% of patients using 34 variables in the ICU. The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. Meanwhile, the AdaBoost algorithm achieved 99.1% sensitivity in the testing dataset. This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. The feature selection shows respiratory rate and mean arterial pressure (MAP) as the most important variables using chi-square test while insulin and fraction of oxygenated hemoglobin are the most important predictors by the mutual information test. ? This is an open access article under the CC BY-NC-SA 4.0 license Final 2025-03-03T07:46:52Z 2025-03-03T07:46:52Z 2024 Article 10.30880/ijie.2024.16.02.012 2-s2.0-85195254601 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195254601&doi=10.30880%2fijie.2024.16.02.012&partnerID=40&md5=df916c6e34c35ef679e8c8da88675cad https://irepository.uniten.edu.my/handle/123456789/37040 16 2 114 122 Penerbit UTHM Scopus |
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Multiple organ failures are the main cause of mortality and morbidity in the intensive care unit (ICU). The progression of organ failures in the ICU is usually monitored using the Sequential Organ Failure Assessment (SOFA) score. This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. Ninety-eight ICU patients? data were obtained retrospectively from Universiti Malaya Medical Centre for analysis. Several machine learning algorithms which are decision tree, linear discriminant, na�ve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. The classifiers were trained on 80% of the patients with 10-fold cross-validations and assessed on 20% of patients using 34 variables in the ICU. The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. Meanwhile, the AdaBoost algorithm achieved 99.1% sensitivity in the testing dataset. This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. The feature selection shows respiratory rate and mean arterial pressure (MAP) as the most important variables using chi-square test while insulin and fraction of oxygenated hemoglobin are the most important predictors by the mutual information test. ? This is an open access article under the CC BY-NC-SA 4.0 license |
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7401823793 |
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7401823793 Shah N.N.H. Razak N.N.A. Razak A.A. Abu-Samah A. Suhaimi F.M. Jamaluddin U. |
| format |
Article |
| author |
Shah N.N.H. Razak N.N.A. Razak A.A. Abu-Samah A. Suhaimi F.M. Jamaluddin U. |
| spellingShingle |
Shah N.N.H. Razak N.N.A. Razak A.A. Abu-Samah A. Suhaimi F.M. Jamaluddin U. Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit |
| author_sort |
Shah N.N.H. |
| title |
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit |
| title_short |
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit |
| title_full |
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit |
| title_fullStr |
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit |
| title_full_unstemmed |
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit |
| title_sort |
machine learning classifications of multiple organ failures in a malaysian intensive care unit |
| publisher |
Penerbit UTHM |
| publishDate |
2025 |
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1826077485671907328 |
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