Machine Learning framework to predict sepsis disease using stacking ensemble algorithm

Abstract

Today, sepsis affects several individuals in the Intensive Care Unit (ICU)because the death rate is increasing dramatically and it has become a huge concern in the area of healthcare. Due to the scarcity of resources, such persons require significant upkeep; this increases the expense of therapy by consuming a large number of resources. In early stages of sepsis, therapy is accessible; however, if treatment is not initiated at the appropriate time, sepsis progresses to an advanced stage, increasing deaths. Several investigations are conducted in order to establish early detection models for sepsis in patients. Machine Learning (ML) and Artificial Intelligence (AI) comprise several applications in the medical area, thanks to breakthroughs in these domains. In this paper, five recent ML approaches, including Decision Tree (DT),Support Vector Machine (SVM), Logistic Regression (LR),Nave Bayes (NB)and-Nearest Neighbour (KNN)is engaged to improve the prediction of model performance utilizing a suggested Stacking Ensemble Meta (SEM) algorithm. Then the suggested SEM combines the best of the two base model prediction accuracy to produce the final prediction. The model is trained using data from the 2019 Physio Net/ Computing in Cardiology Challenge. The model is fine-tuned to get the optimal hyper parameters for training. Accuracy, Recall, Precision, F1-score and ROC-AUC curve are few performance measures utilized to assess the model. The experimental findings showed that the suggested SEM classifier has 0.94 accuracy when compared to the ensemble voting classifier, which has 0.93 accuracy.