Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data.
The most common gynecologic cancer, behind cervical and uterine, is ovarian cancer. Ovarian cancer is a severe concern for women. Abnormal cells form and spread throughout the body. Ovarian cancer microarray data can diagnose and prognosis. Typically, ovarian cancer microarray data contains tens of...
| Publicado en: | BioMed Research International pp. 1 - 17 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/15/2022
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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=158020520&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158020520 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/15/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158020520 158020520 158020520 10.1155/2022/6750457 158020520 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data. aug: au: Kalaiyarasi, M. Rajaguru, Harikumar affil: Bannari Amman Institute of Technology, India sug: subj: Ovarian Neoplasms Diagnosis Ovarian Neoplasms Classification Microarray Analysis Genes Human Analysis of Variance Ovarian Neoplasms Prognosis ab: The most common gynecologic cancer, behind cervical and uterine, is ovarian cancer. Ovarian cancer is a severe concern for women. Abnormal cells form and spread throughout the body. Ovarian cancer microarray data can diagnose and prognosis. Typically, ovarian cancer microarray data contains tens of thousands of genes. In order to reduce computational complexity, selecting the most critical genes or attributes in the entire dataset is necessary. Because microarray datasets have limited samples and many characteristics, classifier detection lags. So, dimensionality reduction measures are essential to protect disease classification genes. In this research, initially the ANOVA method is used for gene selection and then two clustering-based and three transform-based feature extraction methods, namely, Fuzzy C Means, Softmax Discriminant Algorithm (SDA), Hilbert Transform, Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT), respectively, are used to select relevant genes further. Six classifiers further classify the features as normal and abnormal. The NLR classifier gives the highest accuracy for SDA features at 92%, and KNN gives the lowest accuracy of 55% for SDA, Hilbert, and DCT features. With correlation distance feature selection, the NLR classifier attains the lowest accuracy of 53%, and the highest accuracy of 88% is obtained by the GMM classifier. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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