Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data.
Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End R...
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/16/2022
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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=157490983&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157490983 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/16/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157490983 157490983 157490983 10.1155/2022/1467070 157490983 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data. aug: au: Gupta, Surbhi Kalaivani, S. Rajasundaram, Archana Ameta, Gaurav Kumar Oleiwi, Ahmed Kareem Dugbakie, Betty Nokobi affil: Model Institute of Engineering & Technology, Jammu, J&K, India sug: subj: Colonic Neoplasms Prognosis Deep Learning Survival Analysis Treatment Outcomes Human Early Detection of Cancer Surveys United States Neural Networks (Computer) Prediction Models ROC Curve Colonic Neoplasms Classification Magnetic Resonance Imaging Positron-Emission Tomography Hematologic Tests Algorithms ab: Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC). pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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