School of Electronics Engineering, VIT University, Chennai Campus, India.
School of Electronics Engineering, VIT University, Chennai Campus, India.
Comput Biol Med. 2019 May;108:85-92. doi: 10.1016/j.compbiomed.2019.03.017. Epub 2019 Mar 24.
In this paper, we propose a novel Particle Swarm Optimized (PSO) One-Dimensional Convolutional Neural Network with Support Vector Machine (1-D CNN-SVM) architecture for real-time detection and classification of diseases. The performance of the proposed architecture is validated with a novel hardware model for detecting Chronic Kidney Disease (CKD) from saliva samples. For detecting CKD, the urea concentration in the saliva sample is monitored by converting it into ammonia. The urea on hydrolysis in the presence of urease enzyme produces ammonia. This ammonia is then measured using a semiconductor gas sensor. The sensor response is given to the proposed architecture for feature extraction and classification. The performance of the architecture is optimized by regulating the parameter values using a PSO algorithm. The proposed architecture outperforms current conventional methods, as this approach is a combination of strong feature extraction and classification techniques. Optimal features are extracted directly from the raw signal, aiming to reduce the computational time and complexity. The proposed architecture has achieved an accuracy of 98.25%.
在本文中，我们提出了一种新颖的基于粒子群优化（PSO）的一维卷积神经网络与支持向量机（1-D CNN-SVM）架构，用于实时检测和分类疾病。该架构的性能通过一种新型硬件模型进行了验证，该模型用于从唾液样本中检测慢性肾脏病（CKD）。为了检测 CKD，通过将其转化为氨来监测唾液样本中的尿素浓度。在脲酶存在的情况下，尿素在水解时会产生氨。然后使用半导体气体传感器测量这种氨。传感器响应被提供给所提出的架构进行特征提取和分类。通过使用粒子群算法调节参数值来优化架构的性能。与当前的传统方法相比，该架构表现更好，因为这种方法是强大的特征提取和分类技术的结合。从原始信号中直接提取最优特征，旨在减少计算时间和复杂性。所提出的架构的准确率达到了 98.25%。
