Pourahmad Saeedeh, Azad Mohsen, Paydar Shahram
1: Colorectal research center, Shiraz University of Medical Sciences, Shiraz, Iran 2: Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran.
Glob J Health Sci. 2015 Mar 30;7(6):46-54. doi: 10.5539/gjhs.v7n6p46.
To diagnose the malignancy in thyroid tumor, neural network approach is applied and the performances of thirteen batch learning algorithms are investigated on accuracy of the prediction. Therefore, a back propagation feed forward neural networks (BP FNNs) is designed and three different numbers of neuron in hidden layer are compared (5, 10 and 20 neurons). The pathology result after the surgery and clinical findings before surgery of the patients are used as the target outputs and the inputs, respectively. The best algorithm(s) is/are chosen based on mean or maximum accuracy values in the prediction and also area under Receiver Operating Characteristic Curve (ROC curve). The results show superiority of the network with 5 neurons in the hidden layer. In addition, the better performances are occurred for Polak-Ribiere conjugate gradient, BFGS quasi-newton and one step secant algorithms according to their accuracy percentage in prediction (83%) and for Scaled Conjugate Gradient and BFGS quasi-Newton based on their area under the ROC curve (0.905).
为诊断甲状腺肿瘤的恶性程度，应用神经网络方法，并研究了13种批学习算法在预测准确性方面的表现。因此，设计了一个反向传播前馈神经网络（BP FNNs），并比较了隐藏层中三种不同数量的神经元（5个、10个和20个神经元）。分别将患者手术后的病理结果和手术前的临床发现用作目标输出和输入。根据预测中的平均或最大准确率值以及受试者工作特征曲线（ROC曲线）下的面积选择最佳算法。结果显示隐藏层中有5个神经元的网络具有优越性。此外，根据其预测准确率（83%），Polak-Ribiere共轭梯度算法、BFGS拟牛顿算法和一步割线算法表现较好；基于ROC曲线下的面积（0.905），缩放共轭梯度算法和BFGS拟牛顿算法表现较好。
