A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment.
Cancer is caused by the un-controlled division of abnormal cells in a body part. Various cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) threatens the lives of people and today, it is the secondary prime cause of death in women. Numerous research directions conc...
| Publicado en: | Health Care Management Science Vol. 23; no. 3; pp. 414 - 427 |
|---|---|
| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2020
|
| 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=144870386&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144870386 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Sep2020 vid: 23 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144870386 144870386 NLM31686276 10.1007/s10729-019-09498-w NLM31686276 144870386 ppf: 414 ppct: 13 formats: tig: atl: A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment. aug: au: Supriya, M. Deepa, A. J. affil: Anna University, Chennai, India sug: subj: Breast Neoplasms Diagnosis Male Female Algorithms Male Female ab: Cancer is caused by the un-controlled division of abnormal cells in a body part. Various cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) threatens the lives of people and today, it is the secondary prime cause of death in women. Numerous research directions concentrated on the prediction of BC. The prevailing prediction model is time-consuming and have less accuracy. To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN). Primarily, the unprocessed BC data are regarded as the input. The big data (BD) storage comprises some repeated information. Secondarily, such repeated data are eliminated by utilizing Hadoop MapReduce. In the subsequent stage, the data are preprocessed utilizing replacing of missing attributes (RMA) and normalization techniques. Subsequently, the features are generally chosen by utilizing Modified Dragonfly algorithm (MDF). Then, the selected features are inputted for classification. Here, it classifies the features utilizing OANN. Optimization is done by employing the Gray Wolf Optimization (GWO) algorithm. Experiential outcomes are contrasted with prevailing IWDT (Improved Weighted-Decision Tree) in respect of precision, recall, accuracy, and ROC. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|