  Background: Sepsis is a severe condition responsible for many deaths
worldwide. Accurate prediction of sepsis outcomes is crucial for timely and
effective treatment. Although previous studies have used ML to forecast
outcomes, they faced limitations in feature selection and model
comprehensibility, resulting in less effective predictions. Thus, this research
aims to develop an interpretable and accurate ML model to help clinical
professionals predict in-hospital mortality.
  Methods: We analyzed ICU patient records from the MIMIC-III database based on
specific criteria and extracted relevant data. Our feature selection process
included a literature review, clinical input refinement, and using Random
Forest to select the top 35 features. We performed data preprocessing,
including cleaning, imputation, standardization, and applied SMOTE for
oversampling to address imbalance, resulting in 4,683 patients, with admission
counts of 17,429. We compared the performance of Random Forest, Gradient
Boosting, Logistic Regression, SVM, and KNN models.
  Results: The Random Forest model was the most effective in predicting
sepsis-related in-hospital mortality. It outperformed other models, achieving
an accuracy of 0.90 and an AUROC of 0.97, significantly better than the
existing literature. Our meticulous feature selection contributed to the
model's precision and identified critical determinants of sepsis mortality.
These results underscore the pivotal role of data-driven ML in healthcare,
especially for predicting in-hospital mortality due to sepsis.
  Conclusion: This study represents a significant advancement in predicting
in-hospital sepsis mortality, highlighting the potential of ML in healthcare.
The implications are profound, offering a data-driven approach that enhances
decision-making in patient care and reduces in-hospital mortality.
