ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India

Kumar, Rajagopal; Al‐Turjman, Fadi; Srinivas, L. N. B.; Braveen, M.; Ramakrishnan, Jothilakshmi

doi:10.1007/s00521-021-06412-w

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…ANFIS has been used in a variety of current research in the medical field. For instance, the authors in [15][16][17][18] used the ANFIS system to diagnose COVID-19, and the ANFIS model had good accuracy for predicting and diagnosing COVID-19. In [19], the authors proposed an ANFIS model for the prediction of diabetes, and the proposed model attained good results in classification.…”

Section: Related Workmentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination (R2), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model.

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Section: Related Workmentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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“…Kumar R et al [19] proposed Adaptive Neuro-fuzzy Inference System (ANFIS), a machine learning technique to predict the outbreak and track the COVID-19 disease. The system tracks the epidemic growth based on the prior datasets harnessed from cloud computing showing an accuracy of 86%.…”

Section: Related Workmentioning

confidence: 99%

Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence

et al. 2022

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“…Kumar et al. used ANFIS for predicting the peak of the COVID‐19 pandemic and the number of infected cases in India (Kumar et al., 2021 ). Also, Deif et al.…”

Section: The Proposed Integrated Bwm‐llnf‐anfis Approachmentioning

confidence: 99%

Analyzing factors contributing to COVID‐19 mortality in the United States using artificial intelligence techniques

et al. 2022

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Having started since late 2019, COVID‐19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID‐19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best‐Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best‐Worst Method–local linear neuro‐fuzzy–adaptive neuro‐fuzzy inference system approach, the relationship between COVID‐19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.

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ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India

Cited by 12 publications

References 43 publications

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence

Analyzing factors contributing to COVID‐19 mortality in the United States using artificial intelligence techniques

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