An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine

Abstract

As one of the most vulnerable cancers of women, the incidence rate of breast cancer in China is increasing at an annual rate of 3%, and the incidence is younger. Therefore, it is necessary to conduct research on the risk of breast cancer, including the cause of disease and the prediction of breast cancer risk based on historical data. Data based statistical learning is an important branch of modern computational intelligence technology. Using machine learning method to predict and judge unknown data provides a new idea for breast cancer diagnosis. In this paper, an improved optimization algorithm (GSP_SVM) is proposed by combining genetic algorithm, particle swarm optimization and simulated annealing with support vector machine algorithm. The results show that the classification accuracy, MCC, AUC and other indicators have reached a very high level. By comparing with other optimization algorithms, it can be seen that this method can provide effective support for decision-making of breast cancer auxiliary diagnosis, thus significantly improving the diagnosis efficiency of medical institutions. Finally, this paper also preliminarily explores the effect of applying this algorithm in detecting and classifying breast cancer in different periods, and discusses the application of this algorithm to multiple classifications by comparing it with other algorithms.

Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer

“…An innovative concept for the detection of breast cancer is the use of machine learning techniques to forecast and evaluate unknown data. The SVM, combined with the genetic approach, particle swarm optimization, and artificial annealing, creates the enhanced optimization method (GSP_SVM), which is suggested in a study by Dou et al 47 The outcomes demonstrate a very high degree of achievement in categorization, accuracy, etc ., and other measures. When compared to previous optimization algorithms, it is clear that this technique may effectively help decision-making in auxiliary breast cancer diagnosis, greatly enhancing the diagnostic effectiveness of medical institutions.…”

Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor

“…Based on the MCC, AUC, and other metrics, the findings show that a very high level of classification accuracy has been reached. When compared to current optimization methods, this approach may help doctors make better decisions about breast cancer secondary diagnoses, which would improve the diagnosing efficiency of hospitals 32 . Evaluating microscopic images stained with Human Epidermal Growth Factor Receptor 2 (HER2) is difficult, time-consuming, and prone to errors when done manually.…”

Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

“…Genetic algorithms (GAs) are one class of metaheuristic algorithms that were inspired by biological genetic mechanisms to choose optimal solutions [1]. GAs can be applied either as the base classifier, or as an optimized for the parameters of base classifiers [5], or as a feature selector on the data [1].…”