Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO)

Abstract

Breast cancer (BC) is a primary reason for death among the female population around the world. Early identification can aid in decreasing the mortality rates associated with this disease all over the world. The application of big data in healthcare not only saves lives but also saves time and money. Hospital records, patient medical information, and medical exam results are big data sources in the healthcare sector. In this research, a big data-based two-class (i.e., Benign or Cancer) BC classification model is developed using the Deep Reinforcement Learning (DRL) method. The model stages are big data collection, preprocessing, feature selection, classification, and explanations. In the preprocessing step, the data is normalized, and the missing values are replaced. The gorilla troops optimization (GTO) algorithm is employed for the feature selections. Deep reinforcement learning-based Deep Q learning (DQL) is used for the classification, and LIME is used to explain the predicted output. Three different datasets such as WBCD, WDBC, and WPBC from the UCI repository, are used for evaluation. We verify the proposed model against the radial basis function ensemble boosting learning method (RBF-ELB), the Particle Swarm Optimization Multilayer Perceptron (PSO-MLP), and the Genetic Algorithm Multilayer Perceptron (GA-MLP). The results of the experiments show that our method outperforms the traditional methods. The proposed GTO-DQL model achieves 98.90% accuracy for the WBCD dataset, 99.02% for WDBC, and 98.88% for the WPBC dataset, respectively.