A review and comparison of classification algorithms for medical decision making.
Within a health care setting, it is often desirable from both clinical and operational perspective to capture the uncertainty and variability amongst a patient population, for example to predict individual patient outcomes, risks or resource needs. Homogeneity brings the benefits of increased certai...
| Publicado en: | Health Policy Vol. 71; no. 3; pp. 315 - 332 |
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| Autor principal: | |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Mar2005
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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=106621547&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106621547 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01688510 3LC jtl: Health Policy issn: 01688510 maglogo: N pubinfo: dt: Mar2005 vid: 71 iid: 3 pid: 1004 pub: Elsevier B.V. artinfo: ui: 106621547 106621547 2005070923 10.1016/j.healthpol.2004.05.002 NLM15694499 106621547 ppf: 315 ppct: 17 formats: tig: atl: A review and comparison of classification algorithms for medical decision making. aug: au: Harper PR affil: School of Mathematics, University of Southampton, SO17 1BJ, Southampton, UK; p.r.harper@maths.soton.ac.uk sug: subj: Classification Algorithms Evaluation Decision Making, Clinical Analysis of Variance Comparative Studies Discriminant Analysis Regression Human ab: Within a health care setting, it is often desirable from both clinical and operational perspective to capture the uncertainty and variability amongst a patient population, for example to predict individual patient outcomes, risks or resource needs. Homogeneity brings the benefits of increased certainty in individual patient needs and resource utilisation, thus providing an opportunity for both improved clinical diagnosis and more efficient planning and management of health care resources. A number of classification algorithms are considered and evaluated for their relative performances and practical usefulness on different types of health care datasets. The algorithms are evaluated using four criteria: accuracy, computational time, comprehensibility of the results and ease of use of the algorithm to relatively statistically naive medical users. The research has shown that there is not necessarily a single best classification tool, but instead the best performing algorithm will depend on the features of the dataset to be analysed, with particular emphasis on health care data, which are discussed in the paper. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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