جستجوی مقالات مرتبط با کلیدواژه "breast cancer diagnosis" در نشریات گروه "پزشکی"

Breast cancer is the second leading cause of death in women. The advent of machine learning (ML) has opened up a world of possibilities for the discovery and formulation of drugs. It is an exciting development that could revolutionize the pharmaceutical industry. By leveraging ML algorithms, researchers can now identify disease-related targets with greater accuracy. Additionally, ML techniques can be used to predict the toxicity and pharmacokinetics of potential drug candidates.

The main purpose of ML techniques, such as feature selection (FS) and classification, is to develop a learning model based on datasets.

This paper proposed a hybrid intelligent approach using a Binary Grey Wolf Optimization Algorithm and a Self-Organizing Fuzzy Logic Classifier (BGWO-SOF) for breast cancer diagnosis. The proposed FS approach can not only reduce the complexity of feature space but can also avoid overfitting and improve the learning process. The performance of this proposed approach was evaluated on the 10-fold cross-validation technique and the Wisconsin Diagnostic Breast Cancer dataset. Although the performance of breast cancer detection is highly dependent on classification accuracy, most good classification methods have an essential flaw in that they simply seek to maximize the accuracy of classification while ignoring the costs of misclassification among various categories. This is even more important in classification problems when the initial set of features is large. With such a large number of features, it is of special interest to search for a dependency between an optimal number of selected features and the accuracy of the classification model.

In experiments, standard performance evaluation metrics, including accuracy, F-measure, precision, sensitivity, and specificity, were performed. The evaluation resultsdemonstrated that theBGWO-SOFapproach achieves 99.70% accuracy and 99.66% F-measure, which outperforms other state-of-the-art methods.

During the comparison of the results, it was observed that the proposed approach gives better or more competitive results than other state-of-the-art methods. By leveraging the power of MLalgorithms and artificial intelligence (AI) and the findings of the current study, we can optimize the selection of natural pharmaceutical products for the treatment of breast cancer and maximize their efficacy.

Breast cancer is an uncontrolled and unnatural proliferation of cells in different breast tissues. The first measure to diagnose breast cancer is an examination by a surgeon followed by mammography, sonography, sampling, and other diagnosing methods. Given that there are several methods to diagnose breast cancer, and most of them are quite expensive, the present systematic review compares the expenses and effectiveness of different methods to diagnose breast cancer.

The study was carried out as a systematic review through searching databases, i.e., PubMed, Web of Science, Magiran, Scopus, and Embase for articles published from March 1999 to May 31, 2017. The research articles regarding health technology assessment and economic assessment (n = 8) were examined.

Generally, conducting MRI screening and digital mammography every six months after the age of 30 are proved to be the most efficient and economical methods to screen carriers of BRCA (BReast CAncer) mutated genes. Besides, implementing both the techniques simultaneously was more cost-efficient with BRCA1 compared to BRCA2. Some studies have revealed that genetic tests and Oncotype tests, in particular, were the most cost-efficient methods to diagnose the disease, especially in its early stages.

Consequently, indexing gene expression in individuals with BRCA gene mutation is revealed to more cost-efficient.