School of Nursing, Jiangsu Jiankang Vocational College, Nanjing 211800, China.
School of Nursing, Nanjing Medical University, Nanjing 211166, China.
Comput Intell Neurosci. 2022 Sep 23;2022:3373553. doi: 10.1155/2022/3373553. eCollection 2022.
Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable disease among Indian women. According to the WHO, it represents 14% of all malignant growth tumors in women. A couple of studies have been directed utilizing big data to foresee breast malignant growth. Big data is causing a transformation in healthcare, with better and more ideal results. Monstrous volumes of patient-level data are created by using EHR (Electronic Health Record) systems data because of fast mechanical upgrades. Big data applications in the healthcare business will assist with improving results. Conventional forecast models, then again, are less productive in terms of accuracy and error rate because the exact pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and kinds of datasets utilized (trait-based or picture based). This audit article looks at complex information mining, AI, and profound learning models utilized for recognizing breast malignant growth. Since "early identification is the way to avoidance in any malignant growth," the motivation behind this audit article is to support the choice of fitting breast disease expectation calculations, explicitly in the big information climate, to convey powerful and productive results. This survey article analyzes the precision paces of perplexing information mining, AI, and profound learning models utilized for distinguishing breast disease on the grounds that the exactness pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and dataset types (quality based or picture based). The reason for this audit article is to aid the determination of suitable breast disease expectation calculations, explicitly in the big information climate, to convey successful and productive outcomes. Thus, "Early discovery is the way to counteraction in the event of any malignant growth."
近年来，大数据在疾病预测方面在医疗保健领域越来越受欢迎。乳腺癌是城市印度女性以及国际女性中最常见的癌症，受到许多国家和地区的各种事件的影响。乳腺恶性肿瘤是印度女性中一种显著的疾病。根据世界卫生组织的数据，它占女性所有恶性肿瘤的 14%。已经有一些研究利用大数据来预测乳腺癌。大数据正在引起医疗保健的变革，带来更好和更理想的结果。由于快速的机械升级，使用电子健康记录 (EHR) 系统数据会产生大量的患者级数据。医疗保健业务中的大数据应用程序将有助于改善结果。然而，传统的预测模型在准确性和错误率方面的效率较低，因为特定计算的准确速度取决于不同的因素，例如执行结构、数据集（小或大）以及使用的数据集类型（基于特征或基于图像）。本文综述探讨了用于识别乳腺癌的复杂信息挖掘、人工智能和深度学习模型。由于“早期发现是任何癌症预防的关键”，因此本文综述的目的是支持选择合适的乳腺癌疾病预测算法，特别是在大数据环境中，以提供有效和高效的结果。本文综述分析了用于识别乳腺疾病的复杂信息挖掘、人工智能和深度学习模型的精度率，因为特定计算的精度率取决于不同的因素，例如执行结构、数据集（小或大）以及数据集类型（基于特征或基于图像）。本文综述的目的是支持选择合适的乳腺癌疾病预测算法，特别是在大数据环境中，以提供有效和高效的结果。因此，“早期发现是任何癌症预防的关键”。
