An automatic diagnostic system based on deep learning, to diagnose hyperlipidemia
Authors Zhang Q, Liu Y, Liu G, Zhao G, Qu Z, Yang W
Received 17 December 2018
Accepted for publication 8 March 2019
Published 3 May 2019 Volume 2019:12 Pages 637—645
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Konstantinos Tziomalos
Quan Zhang,1,2 Yuliang Liu,1,2 Guohua Liu,3,4 Geng Zhao,5 Zhigang Qu,1,2 Weiming Yang1,2
1College of Electronic Information and Automation; 2Binhai International Advanced Structural Integrity Research Centre, Tianjin University of Science and Technology, Tianjin 300222, People’s Republic of China; 3College of Electronic Information and Optical Engineering; 4Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, NanKai University, Tianjin, People’s Republic of China; 5Tianjin Medical University Hospital for Metabolic Disease, Tianjin 300070, People’s Republic of China
Background: Using artificial intelligence to assist in diagnosing diseases has become a contemporary research hotspot. Conventional automatic diagnostic method uses a conventional machine learning algorithm to distinguish features from which a professional doctor manually extracts features in diagnostic reports. But it can be difficult to collect large amounts of necessary medical data. Therefore, these methods face challenges with efficiency and accuracy.
Method: Here, we proposed an automatic diagnostic system based on a deep learning algorithm to diagnose hyperlipidemia by using human physiological parameters. This model is a neural network which uses technologies of data extension and data correction. Firstly, we corrected and supplemented the original data by the method mentioned previously to solve the problem of lacking data. Secondly, the processed data were used to train a deep learning model. Deep learning model can automatically extract all the available information instead of artificially reducing the raw data. Therefore, it can reduce labor costs. The classifiers classify the data by using features previously mentioned. Finally, the system was evaluated with data from a test dataset.
Result: It achieved 91.49% accuracy, 87.50% sensitivity, 93.33% specificity, and 87.50% precision with data from the test dataset.
Conclusion: The proposed diagnostic method has a highly robust and accurate performance, and can be used for tentative diagnosis. It can automatically diagnose diseases by using human physiological parameters, thereby reducing labor cost, which results in effective improvement of clinical diagnostic efficiency.
Keywords: Auxiliary Diagnosis, Physiological Parameters, Expending Learning Algorithm
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