Optimized model tuning in medical systems

Abstract

For patients considering elective major surgery, information about operative mortality risks is essential for careful decision making. To help patients and surgeons make informed decisions about whether to undergo elective high-risk surgery, a reliable predictive model would be beneficial. This paper focuses on development and optimized tuning of a model predicting risks related to heart interventions of several types. The model is based oil representative data sets collected in the Merged National Registry (MNR) on Cardiovascular Interventions. The registry is operated and governed by the MEDICON Center. The central attention is paid to an instance-based reasoning model and its tuning. In particular, the paper presents and discusses benefits of utilizing a genetic algorithm with limited convergence for this purpose.