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

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% with 99.37% precision. In multi-disease classification, the accuracy achieved is 96.08% with 98.63% precision. The results from both tasks prove the comparative advantage of the proposed BSO-ANFIS algorithm over the competitor models.

Automated summary

This paper proposes a BSO-ANFIS model for heart disease and multi-disease diagnosis, achieving high accuracy and precision by optimizing parameters and analyzing feature extraction. The results demonstrate the superiority of this algorithm over competitor models.