Hybrid PSO feature selection-based association classification approach for breast cancer detection

• Published in: Neural computing & applications Vol. 35; no. 7; pp. 5291 - 5317
• Main Authors: Sowan, Bilal, Eshtay, Mohammed, Dahal, Keshav, Qattous, Hazem, Zhang, Li
• Format: Journal Article
• Language: English
• Published: London Springer London 01-03-2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Associations; Breast cancer; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data mining; Data Mining and Knowledge Discovery; Diagnosis; Feature selection; Image Processing and Computer Vision; Medical diagnosis; Original Article; Particle swarm optimization; Probability and Statistics in Computer Science; Breast cancer prediction; Feature selection; Association classification; Classification
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-07950-7

Breast cancer is one of the leading causes of death among women worldwide. Many methods have been proposed for automatic breast cancer diagnosis. One popular technique utilizes a classification-based association called Association Classification (AC). However, most AC algorithms suffer from considerable numbers of generated rules. In addition, irrelevant and redundant features may affect the measures used in the rule evaluation process. As such, they could severely affect the accuracy rates in rule mining. Feature selection identifies the optimal subset of features representing a problem in almost the same context as the original features. Feature selection is a critical preprocessing step for data mining as it tends to increase the prediction speed and accuracy of the classification model and thereby increase performance. In this research, an ensemble filter feature selection method and a wrapper feature selection algorithm in conjunction with the AC approach are proposed for undertaking breast cancer classification. The proposed approach employs optimal discriminative feature subsets for breast cancer prediction. Specifically, it first utilizes a new bootstrapping search strategy that effectively selects the most optimal feature subset that considers the overall weighted average of the relative frequency-based evaluation criteria function. We employ a Weighted Average of Relative Frequency (WARF)-based filter method to compute discriminative features from the ensemble results. The adopted filter algorithms utilize the prioritization ranking technique for selecting a subset of informative features that are used for subsequent AC-based disease classification. Another wrapper feature selection method, namely a hybrid Particle Swarm Optimization (PSO)-WARF filter-based wrapper method, is also proposed for feature selection. Two classification models, i.e., WARF-Predictive Classification Based on Associations (PCBA) and hybrid PSO-WARF-PCBA, are subsequently constructed based on the above filter and wrapper-based feature selection methods for breast cancer prediction. The proposed approach of the two models is evaluated using UCI breast cancer datasets. The empirical results indicate that our models achieve impressive performance and outperform a variety of well-known benchmark AC algorithms consistently for breast cancer diagnosis.