---
title: "Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve"
authors:
  - "Oscar Luaces"
  - "José Ramón Quevedo"
  - "Francisco Taboada"
  - "Guillermo M. Albaiceta"
  - "Antonio Bahamonde"
doi: ""
url: "https://ijcai.org/proceedings/2007/154"
pdf: "https://ijcai.org/proceedings/2007/0154.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "956-960"
track: ""
keywords:
---

# Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve

Oscar Luaces, José Ramón Quevedo, Francisco Taboada, Guillermo M. Albaiceta, Antonio Bahamonde

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 956-960.

## Abstract

The paper presents a support vector method for estimating probabilities in a real world problem: the prediction of probability of survival in critically ill patients. The standard procedure with Support Vectors Machines uses Platt's method to fit a sigmoid that transforms continuous outputs into probabilities. The method proposed here exploits the difference between maximizing the AUC and minimizing the error rate in binary classification tasks. The conclusion is that it is preferable to optimize the AUC first (using a multivariate SVM) to then fit a sigmoid. We provide experimental evidence in favor of our proposal. For this purpose, we used data collected in general ICUs at 10 hospitals in Spain; 6 of these include coronary patients, while the other 4 do not treat coronary diseases. The total number of patients considered in our study was 2501.