Effect of fuzzy criteria on the performance of decision tree models for heart disease classification Submit This Sample Abstract
Endang Sri Kresnawati (a*), Des Alwine Zayanti (b), Ali Amran (b), Yulia Resti (b)

a) S3 MIPA, Sriwijaya University
Jalan padang selasa no 524 bukit lama Palembang, Indonesia
*eskresna[at]unsri.ac.id
b) Faculty of MIPA, Inderalaya Ogan Ilir, Indonesia

Abstract

Fuzzy decision trees are a development of classical decision trees using fuzzy set theory as the algorithm. A set is said to be fuzzy if its elements are contained in at least two fuzzy sub-sets, which are called criteria. This research aims to measure the performance of a decision tree model that works based on fuzzy criteria. The stages start with discretization, building a decision tree structure, developing decision rules, and evaluating model performance. The results show that the more fuzzy criteria there are, the lower the model performance.

Keywords: fuzzy- decision tree- fuzzy criteria

Topic: Mathematics and Its Applications