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Multiple organ failures are the main cause of mortality and morbidity, especially in intensive care units. The Sequential Organ Failure Assessment (SOFA) score has been used to evaluate organ function for patients in the ICU. This study aims to apply machine learning models for the multiclass classification of mild, moderate, and severe multiple organ failures based on total SOFA score. A sample of 3999 patients was chosen for assessment from the Medical Information Mart for Intensive Care III (MIMIC III) database. The results showed that the bagging algorithm achieved an accuracy of 96.2% for the multiclass classification. Using the correlation feature selection method, the bagging algorithm achieved an accuracy of 91.2% with 84.3% precision and 80.2% recall for the classification of multiple organ failures.

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Classifications of Multiple Organ Failures Using SOFA Score

  • 会议论文

  • 发表日期：

    2025年

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  • Norliyana Nor Hisham Shah,
  • Normy Norfiza Abdul Razak,
  • Athirah Abdul Razak,
  • Ahmad Shahrafidz Khalid,
  • Tong Boon Tang,
  • Mohammad Faizal Ahmad Fauzi,
  • Rashid Jan,
  • Jaspaljeet Singh

摘要

Multiple organ failures are the main cause of mortality and morbidity, especially in intensive care units. The Sequential Organ Failure Assessment (SOFA) score has been used to evaluate organ function for patients in the ICU. This study aims to apply machine learning models for the multiclass classification of mild, moderate, and severe multiple organ failures based on total SOFA score. A sample of 3999 patients was chosen for assessment from the Medical Information Mart for Intensive Care III (MIMIC III) database. The results showed that the bagging algorithm achieved an accuracy of 96.2% for the multiclass classification. Using the correlation feature selection method, the bagging algorithm achieved an accuracy of 91.2% with 84.3% precision and 80.2% recall for the classification of multiple organ failures.