Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks
Tao Liu,
Zhong Ren,
Chengxin Xiong,
Wenping Peng,
Junli Wu,
Shuanggen Huang,
Gaoqiang Liang,
Bingheng Sun
Affiliations
Tao Liu
Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
Zhong Ren
Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China; Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China; Corresponding authorKey Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China.
Chengxin Xiong
Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
Wenping Peng
Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
Junli Wu
Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak–peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.
