Applying Under-Sampling Techniques and Cost-Sensitive Learning Methods on Risk Assessment of Breast Cancer.
Breast cancer is one of the most common cause of cancer mortality. Early detection through mammography screening could significantly reduce mortality from breast cancer. However, most of screening methods may consume large amount of resources. We propose a computational model, which is solely based...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 4; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2015
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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=115925087&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925087 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2015 vid: 39 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925087 115925087 115925087 10.1007/s10916-015-0210-x 115925087 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Applying Under-Sampling Techniques and Cost-Sensitive Learning Methods on Risk Assessment of Breast Cancer. aug: au: Hsu, Jia-Lien Hung, Ping-Cheng Lin, Hung-Yen Hsieh, Chung-Ho affil: Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Tapei City Republic of China sug: subj: Breast Neoplasms Diagnosis Risk Assessment Cancer Screening Models, Statistical Breast Neoplasms Risk Factors Health Information Human Taiwan Sensitivity and Specificity Descriptive Statistics Sampling Methods Experimental Studies Sample Size Costs and Cost Analysis ROC Curve ab: Breast cancer is one of the most common cause of cancer mortality. Early detection through mammography screening could significantly reduce mortality from breast cancer. However, most of screening methods may consume large amount of resources. We propose a computational model, which is solely based on personal health information, for breast cancer risk assessment. Our model can be served as a pre-screening program in the low-cost setting. In our study, the data set, consisting of 3976 records, is collected from Taipei City Hospital starting from 2008.1.1 to 2008.12.31. Based on the dataset, we first apply the sampling techniques and dimension reduction method to preprocess the testing data. Then, we construct various kinds of classifiers (including basic classifiers, ensemble methods, and cost-sensitive methods) to predict the risk. The cost-sensitive method with random forest classifier is able to achieve recall (or sensitivity) as 100 %. At the recall of 100 %, the precision (positive predictive value, PPV), and specificity of cost-sensitive method with random forest classifier was 2.9 % and 14.87 %, respectively. In our study, we build a breast cancer risk assessment model by using the data mining techniques. Our model has the potential to be served as an assisting tool in the breast cancer screening. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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