| Accurate mortality prediction in Intensive Care Units (ICUs) is crucial for optimizing patient care and resource allocation. Traditional prediction models that are essential in guiding clinical decision-making and resource allocation in critical care settings, such as Acute Physiology and Chronic Health Evaluation (APACHE) and Simplified Acute Physiology Score (SAPS), while effective, have limitations that restrain their adaptability and prediction efficacy. Recent paces in Machine Learning (ML), especially in deep learning, present promising opportunities for enhancing prediction accuracy. This study provides a comprehensive evaluation of ML algorithms, encompassing deep learning, for predicting mortality in adult ICUs using certain clinical inputs. Various ML techniques underwent thorough examination, preprocessing, and hyperparameter optimization processes. An ensemble approach combining multiple models, such as CatBoost, LightGBM, Feedforward Neural Networks (FNNs) and Extra Trees, yielded higher performance, achieving an AUC of 0.873 and an accuracy of 81.82%, compared to the respective metrics delivered by the traditional APACHE IV model (AUC: 0.819). The current study bridges gaps in current research by exploring advanced ML methodologies and demonstrates the potential of deep learning in ICU mortality prediction. In doing so, it makes a significant stride towards advancing predictive analytics in healthcare. |
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