A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches

• Published in: Electronics (Basel) Vol. 10; no. 6; p. 699
• Main Authors: Solanki, Yogendra Singh, Chakrabarti, Prasun, Jasinski, Michal, Leonowicz, Zbigniew, Bolshev, Vadim, Vinogradov, Alexander, Jasinska, Elzbieta, Gono, Radomir, Nami, Mohammad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 16.03.2021
• Subjects: Accuracy; Algorithms; Breast cancer; Classification; Classifiers; Data mining; Datasets; Decision trees; Experimentation; Feature selection; Machine learning; Medical diagnosis; Medical prognosis; Medical research; Model accuracy; Multilayer perceptrons; Neural networks; Particle swarm optimization; Performance evaluation; Prognosis; Search algorithms; Sensitivity; Statistics; Support vector machines; Womens health; United States > US; Wisconsin; India
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10060699

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.