This study aims to learn decision rules from input dataset using decision tree learning as a supervised classification method to predict cardiovascular risk level for adult patients (above 30-year-old). Dataset for this study is provided by a blood chemical lab from a private hospital in Southern Jakarta and used under permission. The experiment results using CART algorithm found an optimum decision tree to represent decision rule made previously by a Physician in predicting Cardiovascular risk level. The with a 9 tree-depth of 13 features that achieved 97.3 % training accuracy and 96.8 % testing accuracy respectively. Further decision tree simplification discovers a set of rules to predict level of Cardiovascular risk despite incomplete predictors as input.
DOI: https://doi.org/10.1109/cyberneticscom.2017.8311707
Publish Year: 2017
