Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds : Application in Prognosis of Cardiovascular Mortality

Abstract EN

Machine learning has become a powerful tool for analysing medical domains, assessing the importance of clinical parameters, and extracting medical knowledge for outcomes research.

In this paper, we present a machine learning method for extracting diagnostic and prognostic thresholds, based on a symbolic classification algorithm called REMED.

We evaluated the performance of our method by determining new prognostic thresholds for well-known and potential cardiovascular risk factors that are used to support medical decisions in the prognosis of fatal cardiovascular diseases.

Our approach predicted 36% of cardiovascular deaths with 80% specificity and 75% general accuracy.

The new method provides an innovative approach that might be useful to support decisions about medical diagnoses and prognoses.