BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study. An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years. This dataset consisted of 125 features of 2070 high-risk breast cancer patients. All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm. The performance of the algorithm was identified by studying the machine learning model with the test data. nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective. The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT. BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented. That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice.

中文翻译：

---

使用建模监督机器学习技术的 BRCA1/2 否定性检测风险估计算法的新方法

BRCA1/2基因检测是一项困难、昂贵且耗时的检测，需要过多的工作量。BRCA1/2基因突变的鉴定对于治疗选择和继发性癌症风险具有重要意义。我们旨在开发一种算法，考虑患者的所有临床、人口统计学和遗传特征，以识别本研究中的BRCA1/2阴性。创建了一个实验数据集，收集了 20 年来乳腺癌患者的所有临床、人口统计学和遗传特征。该数据集由 2070 名高危乳腺癌患者的 125 个特征组成。所有数据都进行了数字化和标准化，以检测BRCA1/2机器学习算法中的负性。通过使用测试数据研究机器学习模型来确定算法的性能。发现涉及数据集 2 的 9 个特征的最近邻 (KNN) 和决策树 (DT) 准确率是最有效的。与DT相比，通过减少特征数量去除数据集中不必要的数据可以提高算法的准确率。未执行BRCA1/2就确定了BRCA1/2阴性使用该算法在高危乳腺癌患者中进行基因检测，几分钟内准确率达到 92.88%，避免了检测相关的结果等待压力、时间和金钱损失。建议该算法有助于快速执行患者的治疗计划，并且除了加快临床实践之外，还可以准确地进行。