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Non-Invasive Attributes Significance in the Risk Evaluation of Heart Disease Using Decision Tree Analysis

Shouman, M. and Turner, T.

    Non-invasive attributes are low cost, easy to identify attributes that can have significant influence in the risk evaluation of heart disease. This research formulates a combination of non-invasive attributes that can be used in the risk evaluation of heart disease. It uses the decision tree data mining technique to identify the significance of different single, combined, and calculated non-invasive attribute combinations in the risk evaluation of heart disease against the benchmark Cleveland heart disease dataset and a larger Canberra hospital heart disease dataset. For each non-invasive attribute combination, 10-fold cross-validation is applied to ensure reliable performance measures. Different equations of non-invasive attributes on the Canberra dataset show that the best combination in the risk evaluation of heart disease is age, sex, resting blood pressure and Rohrer’s Index equation with a mean accuracy and standard deviation of 73.8% and 4.9% respectively.
Cite as: Shouman, M. and Turner, T. (2015). Non-Invasive Attributes Significance in the Risk Evaluation of Heart Disease Using Decision Tree Analysis. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 185-193
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