Multi-Class Neural Networks to Predict Lung Cancer.
Lung Cancer is the leading cause of death among all the cancers' in today's world. The survival rate of the patients is 85% if the cancer can be diagnosed during Stage 1. Mining of the patient records can help in diagnosing cancer during Stage 1. Using a multi-class neural networks helps to identify...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 7 |
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| Autores principales: | , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2019
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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=137182959&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137182959 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2019 vid: 43 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137182959 137182959 137182959 10.1007/s10916-019-1355-9 137182959 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-Class Neural Networks to Predict Lung Cancer. aug: au: Rajan, Juliet Rani Chelvan, A. Chilambu Duela, J. Shiny affil: Sathyabama Institute of Science and Technology, Chennai, India sug: subj: Lung Neoplasms Data Mining Neural Networks (Computer) Early Detection of Cancer Machine Learning Neoplasm Staging Survival ab: Lung Cancer is the leading cause of death among all the cancers' in today's world. The survival rate of the patients is 85% if the cancer can be diagnosed during Stage 1. Mining of the patient records can help in diagnosing cancer during Stage 1. Using a multi-class neural networks helps to identify the disease during its stage 1 itself. The implementation of multi-class neural networks has yielded an accuracy of 100%. The model created using the neural networks approach helps to identify lung cancer during Stage 1 itself, thus the survival rate of the patients can be increased. This model can serve as pre-diagnosis tool for the practitioners. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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