A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Abstract

In today’s scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient’s database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability

“…[ 2 ]This approach typically yields higher performance subsets than filter methods by leveraging actual modeling algorithms for evaluation. [ 38 ] Yash Veer Singh et al[ 31 ] applied backward elimination, a wrapper method, effectively removing non-contributory features to identify 11 critical features, achieving a model accuracy of 0.96 with their Ensemble model. Meicheng Yang et al employed forward feature selection, another wrapper strategy, categorizing their 168 selected features into raw features, information missingness, time series, and empiric categories, showcasing the adaptability of wrapper methods in refining feature sets for predictive modeling.…”

Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection

“…Support Vector Machine (SVM): One of the best classi ers, SVM exhibits some degree of linearity. The SVM is supported by sound mathematical intuition and has the ability to handle some nonlinear scenarios by employing a nonlinear basis function [23]. The goal of the Support Vector Machine rule is to create the sole decision boundary or line that may categorize n-dimensional space, allowing us to quickly assign the novel datum to the correct class over the long term [6, 24].…”

Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection

“…One of the best classi ers, SVM exhibits some degree of linearity. The SVM is supported by sound mathematical intuition and has the ability to handle some nonlinear scenarios by employing a nonlinear basis function [23]. The goal of the Support Vector Machine rule is to create the sole decision boundary or line that may categorize n-dimensional space, allowing us to quickly assign the novel datum to the correct class over the long term [6, 24].…”