Possibilistic Assessment of Process-Related Disclosure Risks on the Cloud

Healthcare 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.

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

Singh

Kaur

Batth

et al. 2021

Neural Comput & Applic

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Possibilistic Assessment of Process-Related Disclosure Risks on the Cloud

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References 48 publications

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

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

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