  Early recognition of clinical deterioration (CD) has vital importance in
patients' survival from exacerbation or death. Electronic health records (EHRs)
data have been widely employed in Early Warning Scores (EWS) to measure CD risk
in hospitalized patients. Recently, EHRs data have been utilized in Machine
Learning (ML) models to predict mortality and CD. The ML models have shown
superior performance in CD prediction compared to EWS. Since EHRs data are
structured and tabular, conventional ML models are generally applied to them,
and less effort is put into evaluating the artificial neural network's
performance on EHRs data. Thus, in this article, an extremely boosted neural
network (XBNet) is used to predict CD, and its performance is compared to
eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this
purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate
the models. Moreover, the principal component analysis (PCA) is employed to
verify whether it can improve the adopted models' performance. The performance
of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are
evaluated in CD prediction regarding the accuracy, precision, recall, F1-score,
and geometric mean (G-mean) metrics in a 10-fold cross-validation approach.
According to the experiments, the XGBoost model obtained the best results in
predicting CD among Brazilian hospitals' data.
