The prediction power of machine learning on estimating the sepsis mortality in the intensive care unit

The prediction of sepsis mortality of intensive care unit (ICU) observations using machine learning (ML) methods is hypothesized to yield better or as good as performance compared to the prognostic scores. This paper aims to show that the accuracy of ML in sepsis mortality estimation can be superior and supportive knowledge to SAPS II, APACHE II, and SOFA (traditional) scores even under small sample restrictions. The retrospective collection of data from the patients (n = 200) admitted to ICU of Acibadem Hospital, Istanbul-Turkey, between 2015 and 2020 is utilized to detect the sepsis mortality risk using eight ML methods and a generated ensemble model along with the traditional prognostic scores. The mortality as a decisive indicator is evaluated according to the explanatory variables included quantifying the traditional scores. In the calibration of the data, five different predetermined splits of the random samples are used for the training and the validation of the ML methods. The efficiency of the prediction results of ML methods and the traditional scoring methods are investigated by AUC-ROC curves and other accuracy indicators. Consecutive processes of Box-Cox and Min-Max transformations on data and parameter optimization are performed to increase the efficiency of algorithms. The accuracy in the mortality prediction is achieved the best by the Multi-Layer Perceptron algorithm compared to SAPS II and APACHE II methods and is as good as the one with what SOFA predicts. The prediction power of the best performing ML methods for APACHE II, SAPS II, and SOFA are found to be 84.45%, 85.25% and 73.47%, respectively. The ensemble of eight ML methods is found to increase the performance around 2% in APACHE II score. The outcomes of this study have clinical merits in evaluating the potential use of ML methods in predicting ICU mortality superior to traditional scores APACHE II, SAPS II, and as good as SOFA. Additionally, it explores which of the variables contributing to sepsis mortality risk should be taken as apriori information in treating the patients and requires fewer number of explanatory variables, with reliable prediction powers even for considerably small sample size data sets.