Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering

Abstract

In this paper, a fuzzy expert system based on adaptive neuro-fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p-value of less than 0.05 in chi-square or t-student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.

Sliding-mode-based chaos suppression of coronary artery systems

“…Compared with the dynamic characteristics of the observer (10), Assumption 2 indicates that the uncertainties in ( 2) are slowly time varying such that _ dF0 in (12). Finally, ( 13) can be obtained by substituting (10) into (12).…”

“…The idea behind these methods is to suppress the fatal chaos so as to avoid serious health problems and illness development. 12 For the purpose of effective treatment, the motion states in a vessel with pathological changes have to synchronize with the motion states of a normal vessel through the designed chaos-suppression methods. Therefore, two problems from the aspect of chaos suppression rise up for coronary artery systems.…”

Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System

“…In the paper by M.T. Nouei et al, ANFIS is presented as being able to accurately predict the outcome of a surgery [26]. Another area where this methodology has been used is the incidence of wildfires in terms of the system of Global Warning.…”