A Classification Model Based on an Adaptive Neuro-fuzzy Inference System for Disease Prediction

2020 ◽  
pp. 131-149
Author(s):  
Ricky Mohanty ◽  
Sandeep Singh Solanki ◽  
Pradeep Kumar Mallick ◽  
Subhendu Kumar Pani
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Classification Model ◽  
Disease Prediction ◽  
Inference System ◽  
Model Based ◽  
Neuro Fuzzy

Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver–vehicle delay

2014 ◽  
Vol 8 (4) ◽  
pp. 323-332 ◽  
Author(s):  
Alireza Khodayari ◽  
Ali Ghaffari ◽  
Reinhard Braunstingl ◽  
Fatemeh Alimardani ◽  
Reza Kazemi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Car Following ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Vehicle Delay

scholarly journals Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal

2017 ◽  
Vol 7 (7) ◽  
pp. 668 ◽  
Author(s):  
Moneer Faraj ◽  
Fahmi Samsuri ◽  
Ahmed Abdalla ◽  
Damhuji Rifai ◽  
Kharudin Ali
Keyword(s):  
Eddy Current ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
System Model ◽  
Current Signal ◽  
Inference System ◽  
Model Based ◽  
Neuro Fuzzy

scholarly journals Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system

2021 ◽  
Author(s):  
Parminder Singh ◽  
Avinash Kaur ◽  
Ranbir Singh Batth ◽  
Sukhpreet Kaur ◽  
Gabriele Gianini
Keyword(s):  
Heart Disease ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Medical Decision ◽  
Complex Data ◽  
Swarm Optimization ◽  
Inference System ◽  
Neuro Fuzzy

AbstractHealthcare organizations and Health Monitoring Systems generate large volumes of complex data, which offer the opportunity for innovative investigations in medical decision making. In this paper, we propose a beetle swarm optimization and adaptive neuro-fuzzy inference system (BSO-ANFIS) model for heart disease and multi-disease diagnosis. The main components of our analytics pipeline are the modified crow search algorithm, used for feature extraction, and an ANFIS classification model whose parameters are optimized by means of a BSO algorithm. The accuracy achieved in heart disease detection is$$99.1\%$$99.1%with$$99.37\%$$99.37%precision. In multi-disease classification, the accuracy achieved is$$96.08\%$$96.08%with$$98.63\%$$98.63%precision. The results from both tasks prove the comparative advantage of the proposed BSO-ANFIS algorithm over the competitor models.

scholarly journals A New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering

2014 ◽  
Vol 8 (1) ◽  
pp. 833-838 ◽  
Author(s):  
Feng-Yi Zhang ◽  
Zhi-Gao Liao
Keyword(s):  
Empirical Study ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Prediction Ability ◽  
Inference System ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. The expression of multiinput Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. Empirical study has testified that the proposed model overweighs early work in the aspect of benchmark Box- Jenkins dataset and may provide a practical way to measure the importance of each rule.

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