Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier.
Cervical cancer is the fourth most communal malignant disease amongst women worldwide. In maximum circumstances, cervical cancer indications are not perceptible at its initial stages. There are a proportion of features that intensify the threat of emerging cervical cancer like human papilloma virus,...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
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| Autores principales: | , , , , |
| Formato: | computer program equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138200098&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200098 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200098 138200098 138200098 10.1007/s10916-019-1402-6 138200098 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier. aug: au: Geetha, R. Sivasubramanian, S. Kaliappan, M. Vimal, S. Annamalai, Suresh affil: Bharath Institute of Higher Education and Research, Tamil Nadu, India sug: subj: Cervix Neoplasms Diagnosis ab: Cervical cancer is the fourth most communal malignant disease amongst women worldwide. In maximum circumstances, cervical cancer indications are not perceptible at its initial stages. There are a proportion of features that intensify the threat of emerging cervical cancer like human papilloma virus, sexual transmitted diseases, and smoking. Ascertaining those features and constructing a classification model to categorize, if the cases are cervical cancer or not is an existing challenging research. This learning intentions at using cervical cancer risk features to build classification model using Random Forest (RF) classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA). Utmost medical data sets are frequently imbalanced since the number of patients is considerably fewer than the number of non-patients. For the imbalance of the used data set, SMOTE is cast-off to solve this problem. The data set comprises of 32 risk factors and four objective variables: Hinselmann, Schiller, Cytology and Biopsy. Accuracy, Sensitivity, Specificity, PPA and NPA of the four variables remains accurate after SMOTE when compared with values obtained before SMOTE. An RSOnto ontology has been created to visualize the progress in classification performance. pubtype: Academic Journal doctype: computer program equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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