Performance Evaluation of Various Optimizers for Breast Cancer Diagnosis Using Neural Networks

Abstract

Breast cancer is one of the leading causes of premature death for women worldwide; therefore, early and accurate detection is critical to improving patient outcomes.This study analyzes the effectiveness of multiple neural network optimization algorithms in the classification of breast cancer using clinical data. To assess optimization tools like Adam, Nadam, Adagrad and RMSprop with neural network ,we used a number of efficiency measures,such as accuracy, precision, recall, F1-score, specficity, and ROC-AUC. Our trials, executed at various folds, highlight the positive aspects and drawbacks of each optimizer in relation ti the diagnosis of breast cancer. The results show that Adam Consistently achieves higher balanced accuracy and accuracy than other optimizers. Adam specifically attained a balanced accuracy of 94.12% together with a high accuracy of 94.9%. This research mapped using SDG-3. Our research sheds light on the most effective optimization techniques for creating credible breast cancer diagnosis models.