COVID - 19 SURVIVAL PREDICTION USING SUPERVISED MACHINE LEARNING MODELS.
COVID-19 and related viruses havespread around the world, posing new threats to our society. There is a clear motivation to implement protective measures that aid in the prevention of outbreaks. The effect of the COVID-19 pandemic has prompted a flood of studies aimed at better understanding, tracki...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 3084 - 3088 |
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| Autores principales: | , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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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=151006331&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006331 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006331 151006331 151006331 151006331 ppf: 3084 ppct: 4 formats: fmt: @attributes: type: P tig: atl: COVID - 19 SURVIVAL PREDICTION USING SUPERVISED MACHINE LEARNING MODELS. aug: au: SELVABANUPRIYA, T. MANIKANTA, D. THANMAYASREE NAGA KALYAN, A. VENKATA NAGA SRI SATYA SAI PAVAN MONIKA, M. affil: Department of Computer Science and Engineering BIST, BIHER sug: subj: COVID-19 Survival Machine Learning Utilization Models, Theoretical Early Diagnosis Recovery Forecasting Algorithms Diffusion of Innovation ab: COVID-19 and related viruses havespread around the world, posing new threats to our society. There is a clear motivation to implement protective measures that aid in the prevention of outbreaks. The effect of the COVID-19 pandemic has prompted a flood of studies aimed at better understanding, tracking, and controlling the disease. Machine learning is increasingly becoming more prevalent in the area of medical diagnosis. This can be attributed largely to advancements in disease classification and identification systems, which can provide evidence that assists medical experts in the early detection of deadly diseases, resulting in a dramatic rise in patient survival rates. In this paper, we would like to introduce a method of predicting the probability of survival of an individual infected with COVID-19, which is troubling and widely distributed in the current case scenario, using recent algorithmic improvements that were created to predict death and recovery rates. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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