System Development & Application
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 5
1428-1432

Study on evaluation mechanism of excessive treatment and misdiagnosis of tumor diseases

Zhu Shisheng1
Wang Xinrong1
Mao Liting2
Liu Xueguo2
1. Dept. of Computer, Shantou University, Shantou Guangdong 515063, China
2. The 5th Affiliated Hospital of Sun Yat-Sen University, Zhuhai Guangdong 519000, China
DOI: 10.19734/j.issn.1001-3695.2018.04.0267

Abstract

Aiming at solving the problem of erroneous and excessive medical treatment of tumor diseases, this paper extracted image information from similar medical record experts′ prescriptions based on medical big data, and used a machine learning classification model to quantitatively analyze the level of medical treatment. The program relies on the CT, MRI images of tumor diseases accumulated in the hospital over a long period of time. Based on the tumor type in each treatment case, it selected the corresponding type of image data from the medical database for feature extraction and feature selection, to construct models to obtain a predictor for this tumor disease, to predict the benign and malignant of the current case, and to determine whether there were excessive and erroneous medical problems in the diagnosis process by comparing with the results of the doctor′s diagnosis. The core of the method is to improve the accuracy of discriminating tumors without relying on human beings. Compared with the traditional SVM_RFE, the investigated method which combined SVM_RFE with the Spearman correlation is experimentally proved to perform better in the SVM model of benign and malignant classification of pulmonary nodules. In ge-neral, it offered better performance compared with traditional radiomics method. The solution can detect erroneous and excessive medical treatment issues in real-time and provide warning, which can play a role in supervision and reminding. It potentially reduces the reliance on manual identification and minimizes the burden on patients while preventing and avoiding errors in diagnosis and treatment.

Foundation Support

广东省科技计划资助项目(20140401)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0267
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 5
Section: System Development & Application
Pages: 1428-1432
Serial Number: 1001-3695(2019)05-031-1428-05

Publish History

[2019-05-05] Printed Article

Cite This Article

朱诗生, 汪昕蓉, 毛礼厅, 等. 肿瘤类疾病的过度与错误医疗检查控制机制与模型的研究 [J]. 计算机应用研究, 2019, 36 (5): 1428-1432. (Zhu Shisheng, Wang Xinrong, Mao Liting, et al. Study on evaluation mechanism of excessive treatment and misdiagnosis of tumor diseases [J]. Application Research of Computers, 2019, 36 (5): 1428-1432. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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