期刊
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
卷 2012, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2012/750151
关键词
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资金
- Secretaria de Educacion Publica, Mexico DF, Mexico [PROMEP/103-5/11/4145]
- Venezuelan Grant [FONACIT G-97000726]
- FundaConCiencia
- National Institute on Aging [R01AG036469]
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.
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