Application of $S$-ANFIS method in coronary artery disease

Subbulakshmi, S.; Marimuthu, Ganesan; Neelavathy, N.

doi:10.26637/mjm0s01/0097

Cited by 1 publication

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References 3 publications

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“…An extended version of FIS embedded with neural network and gradient-based learning is the Adaptive Neuro Fuzzy Inference System (ANFIS) [14], which also demonstrated good performance in coronary artery disease prognosis, estimating thermal conductivity enhancement of metal and metal oxide, flood prediction, etc. [15][16][17][18]. Hybrid ANFIS models were also designed to predict heart disease on Statlog database [19].…”

Section: Introductionmentioning

confidence: 99%

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

et al. 2020

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Context and background: Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this paper first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of

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