An Ensemble Dynamic Model and Bio-Inspired Feature Selection Method-based Decision Support System for Predicting Multiple Organ Dysfunction Syndrome in the ICU

Multiple Organ Dysfunction Syndrome (MODS) is one of the most common and severe conditions affecting patients admitted to intensive care units (ICUs). It is characterized by the simultaneous failure or dysfunction of at least two organ systems. Although no specific remedy for MODS has been identified to date, early diagnosis and adequate organ support can significantly improve patient outcomes. Identifying patients at risk of developing MODS in the ICU is challenging. Currently, several methods are used for this purpose, including scoring systems like SOFA and MOD Score, as well as machine learning-based approaches. However, these methods often have limitations. Some require invasive features, making them complex to use in a smart healthcare system. Others suffer from a lack of performance due to various problems, which can potentially lead to unreliable predictions. Feature selection can improve ML models’ performance. Recently, bio-inspired feature selection techniques have shown promise in improving the performance of machine learning methods in many domains, but their effectiveness in MODS prediction has not yet been evaluated. Additionally, research on early MODS prediction, particularly utilizing time-series data and dynamic ensemble methods, remains limited. To fill this gap, the present research used state-of-the-art machine learning algorithms, namely dynamic ensemble techniques, to predict patients at risk of developing MODS in the ICU. Dynamic ensembles are new methods that select an ensemble of the best-performing models for every new test case. We compared the performance of these models with full features and with feature selection. Three nature-inspired meta-heuristic optimization models, namely the binary bat algorithm (BBA), grey wolf optimization (GWO), and genetic algorithm (GA), were evaluated to select the optimal feature subset. The models were built using non-invasive patient features and time-series data from the first 12 hours of ICU admission. The results showed that feature selection significantly improved the performance of dynamic ensemble models. Notably, the METADES model, employing grey wolf optimization for feature selection, demonstrated the best performance in terms of accuracy(96.5%), F1 score (96.4%), precision (97.2%), recall (95.7%), and area under the ROC curve (AUC) (98.4%). These findings highlight the potential and effectiveness of our approach for early MODS prediction in ICUs.

@article{Maach2024,
  title     = {An Ensemble Dynamic Model and Bio-Inspired Feature Selection Method-based Decision Support System for Predicting Multiple Organ Dysfunction Syndrome in the ICU},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {2},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Anas Maach and El Houssine El Mazoudi and Jamila Elalami and Noureddine Elalami},
  doi       = {10.14569/IJACSA.2024.0150285},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150285}
}