SITE, VIT, Vellore, Tamil Nadu, India.
College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia.
Comput Intell Neurosci. 2022 May 16;2022:1871841. doi: 10.1155/2022/1871841. eCollection 2022.
Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.
癌症是一类由细胞异常、失控生长引起的疾病，是全球第二大致死原因。筛查、早期诊断和复发预测为患者提供了成功治疗的最佳机会。然而，这些检测可能既昂贵又具侵入性，并且结果必须由专家进行解读。遗传算法 (GA) 是一种元启发式算法，属于进化算法类。GA 可以在巨大的困难搜索空间中找到最优或近最优解，并且广泛用于搜索和优化。这使它们成为通过创建模型来解释检测结果（尤其是非侵入性检测结果）来检测癌症的理想选择。在本文中，我们全面回顾了现有文献，对其进行了批判性分析，对最新技术进行了比较分析，并由医学专业人员确定了此类技术发展面临的未来挑战。
