@article {
    orvium-670614aa0fe3ae0c1b5bdbd3,
    title = "Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of CardiovascularMortality",
    abstract = "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.",
    keywords = "Machine Learning, Pattern Recognition, Cardiovascular Disease",
    author = "Luis J. Mena",
    year = "2024",
    doi = "10.1155/2012/750151",
    language = "English",
    url = "https://dapp.orvium.io/deposits/670614aa0fe3ae0c1b5bdbd3/view",
}