Discovery of Hidden Patterns in Breast Cancer Patients, Using Data Mining on a Real Data Set.
The aim is to recognize the unknown atterns in a real breast cancer dataset using data mining algorithms as a new method in medicine. Due to excessive missing data in the collection only data on 665 of 809 patients were available. The other missing values were estimated using the EM algorithm in SPS...
| Publicado en: | Studies in Health Technology & Informatics Vol. 262; pp. 142 - 146 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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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=137369808&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137369808 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 262 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 137369808 137369808 137369808 10.3233/SHTI190037 137369808 ppf: 142 ppct: 4 formats: tig: atl: Discovery of Hidden Patterns in Breast Cancer Patients, Using Data Mining on a Real Data Set. aug: au: ATASHI, Alireza TOHIDINEZHAD, Fariba DORRI, Sara NAZERI, Najmeh GHOUSI, Rouzbeh MARASHI, Sina HAJIALIASGARI, Fatemeh affil: E-Health Department, Virtual School, Tehran University of Medical Sciences, Tehran, Iran sug: subj: Data Mining Breast Neoplasms Resource Databases Health Informatics Human Cancer Patients Algorithms Information Science Iran Retrospective Design Female Data Analysis Software Data Analytics Female ab: The aim is to recognize the unknown atterns in a real breast cancer dataset using data mining algorithms as a new method in medicine. Due to excessive missing data in the collection only data on 665 of 809 patients were available. The other missing values were estimated using the EM algorithm in SPSS21 software. Fields have been converted into discrete fields and finally the APRIORI algorithm has been used to analyze and explore the unknown patterns. After the rule extraction, experts in the field of breast cancer eliminated redundant and meaningless relations. 100 association rules with a confidence value of more than 0.9 explored by the APRIORI algorithm and after the clinical expert feedback, 10 clinically meaningful relations have been detected and reported. Due to the high number of risk factors, the use of data mining is effective for cancer data. These patterns provide the future study hypotheses of specific clinical studies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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