An intuitionistic fuzzy diagnosis analytics for stroke disease
Taufik Djatna, Hendradi Hardhienata, Anis Fitri Nur Masruriyah · Journal Of Big Data · 2018
One of the challenges in diagnosing stroke disease is the lack of useful analysis tool to identify critical stroke data that contains hidden relationships and trends from a vast amount of data. In order to address this problem, we proposed Intuitionistic Fuzzy Based Decision Tree in order to diagnosis the different types of stroke disease. The approach is implemented by mapping observation data into Intuitionistic Fuzzy Set. These results lead to a compound of a membership function, non-membership function, and a hesitation degree for each record. The result of Intuitionistic Fuzzy is calculated using Hamming Distance as main requirement for Intuitionistic Fuzzy Entropy. The Hamming Distance calculate the difference between values on the same variable. Main advantage of this approach is that we can find out variables effected on the stroke disease using information gain derived from Intutionistics Entropy. Furthermore, the Intuitionistic Fuzzy based Decision Tree are able to provide plenty of information to stakeholders regarding the hidden facts of established rules and utilize linguistic terms to accommodate unclearness, ambiguity, and hesitation in human perception. The results of Intuitionistic Fuzzy Entropy determine the root and node in the formation of the decision tree model based on the information gain of variables in the data. In this study, simulation results show that the approach successfully determine 20 variables that directly influence stroke. These variables are used to classify the types of stroke. Furthermore, results show that the approach has resulted in 90.59% in classifying stroke disease. Results of the study also demonstrates that the approach produces the best diagnosis performance compared to the other two models according to the accuracy of classification from the type of stroke disease.