Abstract
This study is aimed at the early detection of cardiovascular diseases using predictions learning with a high percentage of successes using the lowest possible number of attributes. Results are comparable to other techniques. Applying the learning system through the reduction of attributes, a tree was obtained with the same classification result of 85.8% that appears in the literature, but it was obtained with 5 variables using the decision tables technique (Decisions Table) and Bayesian Networks.
This work was funded by public research projects of Spanish Ministry of Economy and Competivity (MINECO), reference TEC2017-88048-C2-2-R.
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Solano, R.P., Molina, J.M. (2020). Minimizing Attributes for Prediction of Cardiovascular Diseases. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_50
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