Objectives

In this paper, we propose a computationally efficient Correlational Neural Network (CorrNN) learning model and an automated diagnosis system for detecting Chronic Kidney Disease (CKD). A Support Vector Machine (SVM) classifier is integrated with the CorrNN model for improving the prediction accuracy.

Material and methods

The proposed hybrid model is trained and tested with a novel sensing module. We have monitored the concentration of urea in the saliva sample to detect the disease. Experiments are carried out to test the model with real-time samples and to compare its performance with conventional Convolutional Neural Network (CNN) and other traditional data classification methods.

Results

The proposed method outperforms the conventional methods in terms of computational speed and prediction accuracy. The CorrNN-SVM combined network achieved a prediction accuracy of 98.67%. The experimental evaluations show a reduction in overall computation time of about 9.85% compared to the conventional CNN algorithm.

Conclusion

The use of the SVM classifier has improved the capability of the network to make predictions more accurately. The proposed framework substantially advances the current methodology, and it provides more precise results compared to other data classification methods.

中文翻译：

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用于自动预测慢性肾脏疾病的计算高效的相关神经网络

目标

在本文中，我们提出了一种计算效率高的相关神经网络 (CorrNN) 学习模型和一种用于检测慢性肾病 (CKD) 的自动诊断系统。支持向量机 (SVM) 分类器与 CorrNN 模型集成以提高预测精度。

材料与方法

所提出的混合模型使用新型传感模块进行训练和测试。我们监测了唾液样本中尿素的浓度以检测疾病。进行实验以使用实时样本测试模型，并将其性能与传统的卷积神经网络 (CNN) 和其他传统数据分类方法进行比较。

结果

所提出的方法在计算速度和预测精度方面优于传统方法。CorrNN-SVM 组合网络实现了 98.67% 的预测准确率。实验评估表明，与传统的 CNN 算法相比，整体计算时间减少了约 9.85%。

结论

SVM 分类器的使用提高了网络更准确地进行预测的能力。所提出的框架大大改进了当前的方法，与其他数据分类方法相比，它提供了更精确的结果。