Abstract

Breast cancer in women is a worldwide health problem that is one of the main causes of death. This situation is accentuated in Latin America and the Caribbean countries, where about 159 women die daily from this disease. The World Health Organization recommends focusing on Prevention and Early Detection of cancer to reduce mortality. However, this requires a great deal of information processing and analysis by experts, who require the support of technology to perform these tasks promptly. In recent years, the use of so-called intelligent algorithms has increased to support the fight against breast cancer. The authors summarized the studies published between January 2016 and June 2021, highlighting the current situation and opportunities for Latin America and the Caribbean. Studies were selected using the following terms: intelligent algorithms, assessment metrics, stages of breast cancer control addressed, data sources, data types, female population with breast cancer under study and the countries of the authors who have written articles on this subject. In this study, after applying the inclusion and exclusion criteria 226 articles were selected from a total of 1,105 articles found in the ACM digital library, IEEE Explore, Nature, PubMed, Scopus (Science Direct) and Springer Link databases. Publication between January 2016 and June 2021, breast cancer as main interest, algorithm and data type information, along with compliance with the general question were the inclusion criteria, while, being a research article, compliance with the three subqueries and availability, were the exclusion criteria. Using a spreadsheet as based tool to collect and analyze the data, the study found that the most used elements were: SVM, RF and DT algorithms; accuracy as assessment metric; public information sources; data on tumors (size and shape, among others); USA information sources; India as the country of the first authors who wrote the most articles of the selected papers; and Diagnosis and treatment as the most addressed stage of cancer control. Results in this review paper provide an overview of the application of intelligent algorithms against breast cancer. In this regard, the gaps that were detected are: the Prevention stage of cancer control has not been addressed with intelligent algorithms, and the Early Detection stage has been very little addressed; private data sources could be beneficial in this type of research, but the difficulty in accessing them is a barrier for researchers. In addition, although Latin America and the Caribbean have a significant death rate from breast cancer, patients in this region have not been the subject of study and the participation of researchers on the subject has been almost nonexistent. Finally, there seems to be a great opportunity to generate proposals based on intelligent algorithms with low cost and time to implement that could directly impact patient survival, improving the health systems of the countries in the region.

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