TY -的盟Memon穆罕默德Hammad AU -李,剑萍AU -哈克,阿明Ul盟——Memon穆罕默德Hunain盟——周,王PY - 2019 DA - 2019/11/11 TI -乳腺癌检测物联网健康环境中使用修改后的递归特性选择SP - 5176705六世- 2019 AB -乳腺癌的准确、有效的诊断是非常必要的恢复和治疗在早期阶段物联网医疗环境。物联网已经见证了过渡的生活在过去的几年里,提供了一种方法来分析实时数据和历史数据的新兴角色人工智能和数据挖掘技术。当前最先进的方法不能有效地诊断出乳腺癌在早期阶段,并且大多数女士们遭受了这种危险的疾病。因此,乳腺癌的早期发现明显给医学专家和研究人员带来了一个巨大的挑战。早期发现乳腺癌的来解决这个问题,我们提出了基于机器学习诊断系统有效地分类恶性和良性的物联网环境的人们。在我们提出了系统的发展,机器学习分类器使用支持向量机分类恶性和良性的人。提高分类的性能分类系统,我们使用一个递归特征选择算法从乳腺癌数据集选择更合适的特性。培训/测试分裂法适用于训练和测试分类器的最佳预测模型。此外,分类器的性能一直在检查通过绩效评估指标,如分类、特异性、敏感性,马修斯的相关系数,F1-score和执行时间。为了验证该方法,数据集“威斯康辛诊断乳腺癌”被用于这个研究。 The experimental results demonstrate that the recursive feature selection algorithm selects the best subset of features, and the classifier SVM achieved optimal classification performance on this best subset of features. The SVM kernel linear achieved high classification accuracy (99%), specificity (99%), and sensitivity (98%), and the Matthews’s correlation coefficient is 99%. From these experimental results, we concluded that the proposed system performance is excellent due to the selection of more appropriate features that are selected by the recursive feature selection algorithm. Furthermore, we suggest this proposed system for effective and efficient early stages diagnosis of breast cancer. Thus, through this system, the recovery and treatment will be more effective for breast cancer. Lastly, the implementation of the proposed system is very reliable in all aspects of IoT healthcare for breast cancer. SN - 1530-8669 UR - https://doi.org/10.1155/2019/5176705 DO - 10.1155/2019/5176705 JF - Wireless Communications and Mobile Computing PB - Hindawi KW - ER -