Insight into breast cancer detection: new hybrid feature selection method

Abstract

Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

“… Shaban (2023) developed a hybrid feature selection technique (NHFSM) that uses filter and wrapper approaches to classify breast cancer patients. The technique uses information gain (IG), the hybrid bat algorithm, and particle swarm optimization (HBAPSO) to select nonrepetitive genes.…”

ACNGCNN: Improving Efficiency of Breast Cancer Detection and Progression using Adversarial Capsule Network with Graph Convolutional Neural Networks

“…The advantages of combining several computing approaches to improve classification accuracy are demonstrated by these hybrid models. Two studies that delve into sophisticated feature selection approaches are [7] and [8]. Shaban is concentrating on a novel hybrid feature selection method, whereas C ¸ayır et al present a two-stage deep learning strategy for mitotic recognition.…”

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

“…As shown in Table 1, Shaban et al in [23] developed a new hybrid feature selection method (NHFSM) that takes advantage of the filter and wrapper FS methods. Firstly, in the preselection stage, the relevant nonredundant genes were selected using information gain (IG), a filter method.…”

“…Hybrid methods combine filter and wrapper approaches, and the hybrid methods of FS are popularly used by researchers worldwide. The Hybrid FS approach uses the advantages of the filter and wrapper FS techniques [23][24][25][26][27][28][29][30][31][32][33].…”