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

Miguel-Medina, Telmo and Góngora Alonso, Susel and de la Torre Díez, Isabel and Blanco Sáez, Miriam and Arrechea Elissalt, Héctor Lázaro and Ruigómez Noriega, Atenea and del Río Solá, María Lourdes UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, hector.arrechea@uneatlantico.es, atenea.ruigomez@uneatlantico.es, UNSPECIFIED (2026) Development of a Machine Learning-Based Predictive Model and Clinically Oriented Web Application for 30-Day Mortality Following Cardiac Surgery. Sensors, 26 (5). p. 1656. ISSN 1424-8220

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Official URL: http://doi.org/10.3390/s26051656

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.

Item Type:	Article
Uncontrolled Keywords:	mortality prediction; cardiac surgery; machine learning; XGBoost; clinical decision support; explainability; web application
Subjects:	Subjects > Engineering
Depositing User:	Sr Bibliotecario
Date Deposited:	19 Mar 2026 15:54
Last Modified:	19 Mar 2026 15:54
URI:	http://repositorio.funiber.org/id/eprint/27916

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