Evolutionary Algorithm Pipeline for Improving in Prediction of ‎Earlier Diagnosis of Breast Cancer - Sridevi, G.; Sharmila , K. (International Journal of Basic and Applied Sciences, 2025)
The cells in the breast have been developed in terms of cancer named Breast Cancer (BC), and over the past decade, BC has been the most ‎identified type of cancer in women. Currently, the rapid increase in the predominance of BC may lead to increased mortality worldwide. The ‎disorder is diagnosed manually, which requires efficient expertise and time, both of which are significant in detecting cancer earlier and ‎informing subsequent treatments. There are various Machine Learning (ML) methods that assist in making several decisions as well as ‎performing diagnoses from the collected data, but have failed in accomplishing high prediction in diagnosing BC. This paper focuses on ‎investigating the probability of Ensemble Learning (EL) with Evolutionary Algorithm (EA) for risk prediction and BC categorization, ‎precisely through exact feature selection and extraction. Moreover, the key focus is on creating a consistent and precise model by associating ‎the best features of various learning algorithms and ensembling the metadata sets. Therefore, this research focuses on analyzing methods to ‎improve the accuracy of BC diagnosis through a proposed ensemble Artificial Neural Network (ANN) method with a Keras-based ‎approach. Hence, the evaluated results propose a method determined through accuracy metrics, which are compared with the Boruta method ‎and the ANN method. The accuracy of the Keras-based ensemble ANN method, at 97.37%, is higher than that of other ML methods‎.