Today, diabetes is one of the most common, chronic, and, due to some complications, deadliest diseases in the world. The early detection of diabetes is very important for its timely treatment since it can stop the progression of the disease. The proposed method can help not only to predict the occurrence of diabetes in the future but also to determine the type of the disease that a person experiences. Considering that type 1 diabetes and type 2 diabetes have many differences in their treatment methods, this method will help to provide the right treatment for the patient. By transforming the task into a classification problem, our model is mainly built using the hidden layers of a deep neural network and uses dropout regularization to prevent overfitting. We tuned a number of parameters and used the binary cross-entropy loss function, which obtained a deep neural network prediction model with high accuracy. The experimental results show the effectiveness and adequacy of the proposed DLPD (Deep Learning for Predicting Diabetes) model. The best training accuracy of the diabetes type data set is 94.02174%, and the training accuracy of the Pima Indians diabetes data set is 99.4112%. Extensive experiments have been conducted on the Pima Indians diabetes and diabetic type datasets. The experimental results show the improvements of our proposed model over the state-of-the-art methods.

如今，糖尿病已成为世界上最常见的慢性疾病之一，并且由于某些并发症而成为最致命的疾病。糖尿病的早期发现对于及时治疗非常重要，因为它可以阻止疾病的进展。所提出的方法不仅可以帮助预测未来糖尿病的发生，而且可以帮助确定人所经历的疾病类型。考虑到1型糖尿病和2型糖尿病在治疗方法上有许多差异，因此该方法将有助于为患者提供正确的治疗方法。通过将任务转化为分类问题，我们的模型主要是使用深层神经网络的隐藏层构建的，并使用辍学正则化来防止过度拟合。我们调整了许多参数，并使用了二进制交叉熵损失函数，从而获得了具有较高准确性的深度神经网络预测模型。实验结果表明了所提出的DLPD（预测糖尿病的深度学习）模型的有效性和充分性。糖尿病类型数据集的最佳训练准确性为94.02174％，比马印第安人糖尿病数据集的训练准确性为99.4112％。已经对比马印第安人糖尿病和糖尿病类型数据集进行了广泛的实验。实验结果表明，相对于最新方法，我们提出的模型有所改进。皮马印第安人糖尿病数据集的训练准确性为99.4112％。已经对比马印第安人糖尿病和糖尿病类型数据集进行了广泛的实验。实验结果表明，我们提出的模型相对于最新方法有所改进。皮马印第安人糖尿病数据集的训练准确性为99.4112％。已经对比马印第安人糖尿病和糖尿病类型数据集进行了广泛的实验。实验结果表明，我们提出的模型相对于最新方法有所改进。