Background/Objectives: This paper enlightens the use of the renowned Bacterial Foraging Optimization (BFOA) Algorithm to classify and identify the occurrence of disease in an exact person. Methods/Statistical analysis: The classical BFOA predominantly relies on the chemotactic steps of the bacteria. The extracted fine-tuned ST segment data from the ElectroCardioGraph (ECG) signal is presented to the algorithm to find its best position. The search domain and the chemotactic step count are increased centered on the number of ST data delivered. To promote the varied chemotactic stages an external frequency is driven into the process to prove the system with more sensitivity. When the bacterium is close to the optimal value it oscillates and ensue further to investigate the global optima. Findings: Two structures are laid to improve and determine the exact global optima; 1. External frequency and 2. Adaptive chemotactic step, which deliver a better convergence rate. The system confirms an accuracy of 97% with 92.41% of sensitivity and 95.21% of positive predictive accuracy. The best position is found with a minimum chemotactic step and reproduction stages. Applications/Improvements: The proposed method can be used as a clinical assistant to procure a precise decision the field of cardiology, with more chemotactic steps the system can be improved to produce high accurate results.
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