Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches

Cervical cancer, as the fourth most common cause of death from cancer among women, has no symptoms in the early stage. There are few methods to diagnose cervical cancer precisely at present. Support vector machine (SVM) approach is introduced in this paper for cervical cancer diagnosis. Two improved...

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Bibliographic Details
Published inIEEE access Vol. 5; pp. 25189 - 25195
Main Authors Wu, Wen, Zhou, Hao
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Biopsy
Cancer
Cervical cancer
Cytology
data-driven
Diagnosis
Ground truth
Prediction algorithms
Pregnancy
Principal component analysis
Principal components analysis
Risk analysis
Support vector machines
SVM classification
SVM-PCA
SVM-RFE
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Summary:Cervical cancer, as the fourth most common cause of death from cancer among women, has no symptoms in the early stage. There are few methods to diagnose cervical cancer precisely at present. Support vector machine (SVM) approach is introduced in this paper for cervical cancer diagnosis. Two improved SVM methods, support vector machine-recursive feature elimination and support vector machine-principal component analysis (SVM-PCA), are further proposed to diagnose the malignant cancer samples. The cervical cancer data are represented by 32 risk factors and 4 target variables: Hinselmann, Schiller, Cytology, and Biopsy. All four targets have been diagnosed and classified by the three SVM-based approaches, respectively. Subsequently, we make the comparison among these three methods and compare our ranking result of risk factors with the ground truth. It is shown that SVM-PCA method is superior to the others.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2763984