Today, machine learning plays a major role in different branches of the healthcare industry, from prognosis and diagnosis to drug development providing a significant perspective on the medical landscape for disease prevention or treatment and the improvement of human life. Recently, the use of deep neural networks in different machine learning applications has shown a great contribution to the improvement of the accuracy of predictions. In this paper, a novel application of convolutional neural networks on medical prognosis is presented. The proposed method employs a one-dimensional convolutional neural network (1D-CNN) to predict the survivability of breast cancer patients. After further examining the network architecture, a number of 8, 14 and 24 convolutional filters were considered within three layers, respectively, followed by a max-pooling layer after the second and third layers. In addition, regarding the probabilistic nature of the survivability prediction problem, an extra layer was added to the network in order to calculate the probability of the patient survivability. To train the developed 1D-CNN machine, the SEER database as the most reliable repository of cancer survivability was used to retrieve the required training set. After a pre-processing to remove unusable records, a set of 50 000 breast cancer cases including 35 features was prepared for training the machine. Based on the results obtained in this study, the developed machine could reach an accuracy of 85.84%. This accuracy is the highest level of accuracy compared to the previous prediction methods. Furthermore, the mean squared error of the calculated probability was 0.112, which is an acceptable value of error for a probability calculation machine. The output of the developed machine can be used reliably by physicians to make decision about the most appropriate treatment strategy.

中文翻译：

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用于预测乳腺癌生存能力的一维概率卷积神经网络

今天，机器学习在医疗保健行业的不同分支中发挥着重要作用，从预后和诊断到药物开发，为疾病预防或治疗以及改善人类生活的医学前景提供了重要视角。最近，深度神经网络在不同的机器学习应用中的使用显示出对提高预测准确性的巨大贡献。在本文中，提出了卷积神经网络在医学预后中的新应用。所提出的方法采用一维卷积神经网络（1D-CNN）来预测乳腺癌患者的生存能力。在进一步检查网络架构后，分别在三层内考虑了 8、14 和 24 个卷积滤波器，在第二层和第三层之后是最大池化层。此外，关于生存能力预测问题的概率性质，在网络中添加了一个额外的层，以计算患者生存能力的概率。为了训练开发的 1D-CNN 机器，使用 SEER 数据库作为最可靠的癌症生存能力库来检索所需的训练集。经过预处理以删除不可用的记录后，准备了一组包含 35 个特征的 50 000 个乳腺癌病例，用于训练机器。根据本研究获得的结果，开发的机器可以达到 85.84% 的准确率。与之前的预测方法相比，该准确度是最高的准确度。此外，计算概率的均方误差为 0.112，这是概率计算器可接受的误差值。医生可以可靠地使用开发机器的输出来决定最合适的治疗策略。