Breast cancer is one of the most common types of cancer among Jordanian women. Recently, healthcare organizations in Jordan have adopted electronic health records, which makes it feasible for researchers to access huge amounts of medical records. The goal of this study is to predict the recurrence of breast cancer using machine learning algorithms. We developed a Natural Language Processing algorithm to extract key features about breast cancer from medical records at King Abdullah University Hospital (KAUH) in Jordan. We integrated these features and built a medical dictionary for breast cancer. We applied multiple machine learning algorithms on the extracted information to predict the recurrence of breast cancer in patients. Our predicted results were approved by specialist physicians from KAUH. The medical dictionary was created and the accuracy of the data had been validated by targeted users (physicians, researchers). This dictionary can be used for personalized medicine. All machine learning algorithms had a nice performance. OneR algorithm has the best balance of sensitivity and specificity. The medical dictionary will help physicians to choose the most appropriate treatment plan in a short time. The machine learning prediction results can help physicians to make the correct clinical decision regarding their treatment options.

乳腺癌是约旦妇女中最常见的癌症类型之一。最近，约旦的医疗保健组织采用了电子健康记录，这使得研究人员可以访问大量的医疗记录。这项研究的目的是使用机器学习算法预测乳腺癌的复发。我们开发了一种自然语言处理算法，以从约旦国王阿卜杜拉大学医院（KAUH）的病历中提取有关乳腺癌的关键特征。我们整合了这些功能，并为乳腺癌建立了医学词典。我们在提取的信息上应用了多种机器学习算法，以预测患者乳腺癌的复发。我们的预测结果得到了KAUH的专科医生的认可。创建了医学词典，并且数据的准确性已由目标用户（医师，研究人员）验证。这本词典可以用于个性化医学。所有机器学习算法都具有良好的性能。OneR算法在灵敏度和特异性之间达到最佳平衡。医学词典将帮助医生在短时间内选择最合适的治疗方案。机器学习预测结果可以帮助医生做出有关其治疗选择的正确临床决策。医学词典将帮助医生在短时间内选择最合适的治疗方案。机器学习预测结果可以帮助医生做出有关其治疗选择的正确临床决策。医学词典将帮助医生在短时间内选择最合适的治疗方案。机器学习预测结果可以帮助医生做出有关其治疗选择的正确临床决策。