Predicting Cervical Carcinoma Stages Identification using SVM Classifier

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-22 Number-3
Year of Publication : 2015
Authors : Chandra J, Nachamai.M, Anitha S Pillai


Chandra J, Nachamai.M, Anitha S Pillai "Predicting Cervical Carcinoma Stages Identification using SVM Classifier". International Journal of Computer Trends and Technology (IJCTT) V22(3):122-125, April 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Cervical Cancer is one of the most specific cancers among women global. This paper is a candid attempt to cover a small gap in cervical cancer research practice in India. Longitudinal studies are one area under which more resourceful data is required to treat on the patients affected. This work would prove evidential for an effective intervention and systematic evaluation to assess the impact short term and long term carcinoma. The International federation of Gynaecology and Obstetrics staging of cervical carcinomas are into four stages: Invasive cancer, Clinical lesions, Pelvis, and True pelvis. In Machine learning, research has concrete proof that support vector machine is a better approach to learn any data base, SVM classification method performance is superior to any other commonalities like bagging, boosting. The upshots corroborated to be tangibly supporting the original diagnosis given by the IGCS. This modus espoused has proven that it is indubitable investigative support for diagnosing of cervical carcinoma staging. The results of staging classification can be used by the clinical specialists to decide upon the remedial course of treatment of carcinoma.

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Cervical Cancer (CC), Support Vector Machine (SVM), federation of Gynaecology and Obstetrics staging (FGOS), low and middle income countries (LMICs).