A CHAOTIC MAP-DRIVEN JAYA ALGORITHM FOR ROBUST CLINICAL ATTRIBUTES SELECTION IN BREAST CANCER METASTASIS PREDICTION

Abstract

Breast cancer (BC) remains a leading cause of cancer-related mortality globally, especially due to its metastatic tendencies. Metastatic breast cancer (MBC) occurs when cancer cells spread from the breast to other parts of the body, complicating treatment and reducing survival rates. Predicting MBC is crucial for timely intervention, but challenges persist due to noisy features that limit model accuracy. While traditional machine learning techniques have been applied to predict MBC, they often struggle with identifying key factors for accurate prediction. In this study, we propose enhancing the JAYA algorithm by incorporating a sinusoidal chaotic map for population initialization and to improve exploration during optimization. Specifically, the sinusoidal chaotic initialization is employed to generate a more uniformly distributed and diverse initial population, thereby improving search space coverage, reducing premature convergence, and enhancing the algorithm’s ability to identify relevant features for MBC prediction.  The enhanced JAYA algorithm was combined with an artificial neural network (ANN) for prediction, and its performance was evaluated using a 5-year MBC dataset obtained from the Kaggle repository, comprising 6,726 instances, 26 features, and a binary class label. The results indicate that the proposed method achieves improved performance across key evaluation metrics, including accuracy, F1 score, sensitivity, and specificity, when compared with the baseline study of Muhammed et al., (2024). Specifically, the proposed method attained an accuracy of 79.76%, an F1 score of 80.13%, a sensitivity of 81.88%, and a specificity of 77.52%, compared to 79.28%, 79.66%, 81.04%, and 77.51%, respectively, reported in the baseline study, thereby demonstrating consistent performance improvements across all evaluation metrics. These improvements were confirmed to be statistically significant using a T-test. Hyperparameter tuning, including adjustments to population size and iteration count, further optimized the method’s performance, confirming the benefits of fine-tuning in metaheuristic algorithms for MBC prediction.