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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| Format: | Article |
| Language: | English |
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Penerbit UTHM
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44737/ http://umpir.ump.edu.my/id/eprint/44737/1/Machine%20learning%20classifications%20of%20multiple%20organ%20failures%20in%20a%20malaysian.pdf |
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| author | Norliyana, Nor Hisham Shah Normy Norfiza, Abdul Razak Athirah, Abdul Razak Asma’, Abu-Samah Fatanah, M. Suhaimi Ummu Kulthum, Jamaludin |
| author_facet | Norliyana, Nor Hisham Shah Normy Norfiza, Abdul Razak Athirah, Abdul Razak Asma’, Abu-Samah Fatanah, M. Suhaimi Ummu Kulthum, Jamaludin |
| author_sort | Norliyana, Nor Hisham Shah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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, naïve 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. |
| first_indexed | 2025-11-15T03:56:27Z |
| format | Article |
| id | ump-44737 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:56:27Z |
| publishDate | 2024 |
| publisher | Penerbit UTHM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-447372025-06-06T03:32:37Z http://umpir.ump.edu.my/id/eprint/44737/ Machine learning classifications of multiple organ failures in a malaysian intensive care unit Norliyana, Nor Hisham Shah Normy Norfiza, Abdul Razak Athirah, Abdul Razak Asma’, Abu-Samah Fatanah, M. Suhaimi Ummu Kulthum, Jamaludin QA75 Electronic computers. Computer science TJ Mechanical engineering and machinery 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, naïve 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. Penerbit UTHM 2024 Article PeerReviewed pdf en cc_by_nc_sa_4 http://umpir.ump.edu.my/id/eprint/44737/1/Machine%20learning%20classifications%20of%20multiple%20organ%20failures%20in%20a%20malaysian.pdf Norliyana, Nor Hisham Shah and Normy Norfiza, Abdul Razak and Athirah, Abdul Razak and Asma’, Abu-Samah and Fatanah, M. Suhaimi and Ummu Kulthum, Jamaludin (2024) Machine learning classifications of multiple organ failures in a malaysian intensive care unit. International Journal of Integrated Engineering, 16 (2). pp. 114-122. ISSN 2229-838X. (Published) https://doi.org/10.30880/ijie.2024.16.02.012 https://doi.org/10.30880/ijie.2024.16.02.012 |
| spellingShingle | QA75 Electronic computers. Computer science TJ Mechanical engineering and machinery Norliyana, Nor Hisham Shah Normy Norfiza, Abdul Razak Athirah, Abdul Razak Asma’, Abu-Samah Fatanah, M. Suhaimi Ummu Kulthum, Jamaludin Machine learning classifications of multiple organ failures in a malaysian intensive care unit |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/44737/ http://umpir.ump.edu.my/id/eprint/44737/ http://umpir.ump.edu.my/id/eprint/44737/ http://umpir.ump.edu.my/id/eprint/44737/1/Machine%20learning%20classifications%20of%20multiple%20organ%20failures%20in%20a%20malaysian.pdf |