Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

Purpose: One of the leading causes of death among women is breast cancer. However, it has been established that early diagnosis with accurate results can ensure the prolonged survival of patients even with the illness. Deep learning (DL) and expert systems have been proven beneficial and gaining popularity in breast cancer diagnosis because of their effective taxonomy and high diagnostic capability. Method: This paper proposes a DL-based breast cancer model empowered with a rule-based hybrid feature selection mechanism to remove irrelevant features, thus proving to be a catalyst for improving diagnostic accuracy. The DL-based enabled feature selection helps in key attributes that are relevant to the diagnosis of breast cancer. The model has been tested utilizing the well-known Wisconsin Breast Cancer Dataset (WBCD) and validated through performance measures such as accuracy, sensitivity, specificity, F-score, and ROC curves. Results: The experimental results revealed that the DL-based enabled with feature selection performed excellently when compared with existing models on breast cancer using the same dataset. The findings show a greater diagnostic accuracy of 99.5% and detect five insightful features with a significant clue for better diagnosis. Conclusion: The proposed model can predict the presence of breast cancer by identifying the most relevant features in the diagnosis of breast cancer. The system looks promising when compared to other existing models for breast cancer.