  This paper presents a unique solution to challenges in medical image
processing by incorporating an adaptive curve grey wolf optimization (ACGWO)
algorithm into neural network backpropagation. Neural networks show potential
in medical data but suffer from issues like overfitting and lack of
interpretability due to imbalanced and scarce data. Traditional Gray Wolf
Optimization (GWO) also has its drawbacks, such as a lack of population
diversity and premature convergence. This paper addresses these problems by
introducing an adaptive algorithm, enhancing the standard GWO with a sigmoid
function. This algorithm was extensively compared to four leading algorithms
using six well-known test functions, outperforming them effectively. Moreover,
by utilizing the ACGWO, we increase the robustness and generalization of the
neural network, resulting in more interpretable predictions. Applied to the
publicly accessible Cleveland Heart Disease dataset, our technique surpasses
ten other methods, achieving 86.8% accuracy, indicating its potential for
efficient heart disease prediction in the clinical setting.
