# Development of a Machine Learning-Based Predictive Model and Clinically Oriented Web Application for 30-Day Mortality Following Cardiac Surgery

## TL;DR

This paper presents a machine learning model and web app to predict 30-day mortality after cardiac surgery, aiding clinicians in preoperative risk assessment.

## Contribution

A novel machine learning model and clinician-friendly web application for real-time mortality prediction in cardiac surgery patients.

## Key findings

- XGBoost achieved high performance with an AUC-ROC of 0.968 and Brier score of 0.058.
- A web application using StreamLit was developed for real-time predictions with SHAP-based explainability.
- Clinicians found the tool intuitive and useful for preoperative risk assessment.

## Abstract

This study aimed to develop and validate a machine learning-based model for predicting 30-day mortality in cardiac surgery patients and to implement a functional, clinician-oriented web application that enables the real-time use of the model. A retrospective cohort of 325 cardiac surgery patients was analysed using supervised machine learning. After preprocessing and clinical feature selection, several models were trained and evaluated through cross-validation. XGBoost achieved the best results, with an AUC-ROC of 0.968, recall of 0.800, and Brier score of 0.058. To facilitate clinical usability, a web-based application was developed using StreamLit, enabling clinicians to input patient data and predict mortality in real time. The application includes SHAP-based explainability for each prediction, thereby ensuring model transparency. Preliminary feedback from clinicians indicated that the tool was intuitive and informative and showed potential for preoperative risk assessment. The integration of a robust ML (machine learning) model with a functional clinical application offers a practical tool for supporting decision-making in cardiac surgery. This combined approach enhances both accuracy and accessibility, which are key to real-world impacts. Future work will involve multicentre validation and user-centred refinement.