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 OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1207-1213
Author(s):  
X. Y. Zhang ◽  
B. Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Deformation Prediction ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Dam Deformation

Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of ANFIS. Based on the deformation data from the Xiaolangdi Dam in China, three neural network algorithms, ANFIS, PSO-ANFIS, and CPSO-ANFIS, are used to establish the dam deformation prediction models after data preparation and selection of influencing factors for the dam deformation. The results are compared using evaluation indicators that show that CPSO-ANFIS is more accurate and stable than ANFIS and PSO-ANFIS both in predictive ability and in predicted results.

scholarly journals The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set

MATEMATIKA ◽  
2017 ◽  
Vol 33 (1) ◽  
pp. 11
Author(s):  
Mamman Mamuda ◽  
Saratha Sathasivan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Disease Diagnosis ◽  
Learning Ability ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Linguistic Rules ◽  
Gradient Descent Algorithm

Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.

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