Interaction-Based Feature Selection Technique Using Fuzzy Discretization and Class Association Rule Mining for Breast Cancer Classification

Breast cancer is a leading global cause of cancer-related deaths highlighting the need for an accurate diagnostic system. Up to now, computer-aided diagnosis (CAD) system plays an essential role in supporting pathologists with prompt and accurate classification. Feature selection within the CAD system is crucial as it helps identify the most relevant data for subsequent classification tasks. This paper proposed a novel method that focuses on fuzzy discretization in handling continuous features and selecting relevant and interactive features while eliminating redundancy using Class Association Rule Mining (CARM). The proposed method, FD-CARI was compared with other feature selection techniques including CFS, FCBF, Consistency, Relief-F, and mRMR using five different machine learning classifiers such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). Performance evaluation metrics such as Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), Precision, F1-Score, and AUC were then utilized. Results: The experimental findings consistently showed that the proposed method achieved high performance with an ACC of 96.21%, SEN of 94.26%, and SPE of 97.38% on the SVM classifiers, and an ACC of 96.05%, SEN of 93.82%, and SPE of 97.38% on the LR classifiers. It demonstrated similar effectiveness to Relief-F for DT and RF classifiers. However, FCBF achieved the highest performance on NB with ACC, SEN, and SPE values of 96.21%, 92%, and 96.60%, respectively. The proposed method efficiently selects relevant and interactive features while enabling classifiers to achieve better classification accuracy.