Support vector machine for the diagnosis of malignant mesothelioma

被引：0
|
作者：
Ushasukhanya, S. [1 ]
Nithyakalyani, A. [1 ]
Sivakumar, V [2 ]
机构：
[1] SRM Inst Sci & Technol, Dept Comp Sci Engn, Madras, Tamil Nadu, India
[2] SRM Inst Sci & Technol, Dept Software Engn, Madras, Tamil Nadu, India
来源：
PROCEEDINGS OF THE 10TH NATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND ITS APPLICATIONS (NCMTA 18) | 2018年 / 1000卷
关键词：
D O I：
10.1088/1742-6596/1000/1/012153
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma's sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.
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页数：6
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