Cross-industry standard process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management
Clin. transl. oncol. (Print)
; 14(1): 73-79, ene. 2012. tab, ilus
Article
en En
| IBECS
| ID: ibc-126104
Biblioteca responsable:
ES1.1
Ubicación: BNCS
ABSTRACT
OBJECTIVES:
The aim of this study was to assess the applicability of knowledge discovery in database methodology, based upon data mining techniques, to the investigation of lung cancer surgery.METHODS:
According to CRISP 1.0 methodology, a data mining (DM) project was developed on a data warehouse containing records for 501 patients operated on for lung cancer with curative intention. The modelling technique was logistic regression.RESULTS:
The finally selected model presented the following values sensitivity 9.68%, specificity 100%, global precision 94.02%, positive predictive value 100% and negative predictive value 93.98% for a cut-off point set at 0.5. A receiver operating characteristic (ROC) curve was constructed. The area under the curve (CI 95%) was 0.817 (0.740- 0.893) (p < 0.05). Statistical association with perioperative mortality was found for the following variables [odds ratio (CI 95%)] age over 70 [2.3822 (1.0338-5.4891)], heart disease [2.4875 (1.0089-6.1334)], peripheral arterial disease [5.7705 (1.9296-17.2570)], pneumonectomy [3.6199 (1.4939-8.7715)] and length of surgery (min) [1.0067 (1.0008-1.0126)].CONCLUSIONS:
The CRISP-DM process model is very suitable for lung cancer surgery analysis, improving decision making as well as knowledge and quality management (AU)
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Colección:
06-national
/
ES
Base de datos:
IBECS
Asunto principal:
Calidad de la Atención de Salud
/
Procedimientos Quirúrgicos Pulmonares
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Conocimiento
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Toma de Decisiones
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Minería de Datos
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Neoplasias Pulmonares
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Modelos Teóricos
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Aged
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Female
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Humans
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Male
Idioma:
En
Revista:
Clin. transl. oncol. (Print)
Año:
2012
Tipo del documento:
Article