Data Mining for Cardiovascular Disease Prediction.
Cardiovascular diseases (CVDs) aredisorders of the heart and blood vessels and are a major cause of disability and premature death worldwide. Individuals at higher risk of developing CVD must be noticed at an early stage to prevent premature deaths. Advances in the field of computational intelligenc...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 1; pp. 1 - 9 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
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=147997206&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147997206 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2021 vid: 45 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 147997206 147997206 147997206 10.1007/s10916-020-01682-8 147997206 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Data Mining for Cardiovascular Disease Prediction. aug: au: Martins, Bárbara Ferreira, Diana Neto, Cristiana Abelha, António Machado, José affil: University of Minho, Campus of Gualtar, 4710, Braga, Portugal sug: subj: Data Mining Methods Cardiovascular Diseases Prognosis Human Data Analysis ROC Curve Decision Support Systems, Management Sensitivity and Specificity Descriptive Statistics Quality Improvement Health Information Systems ab: Cardiovascular diseases (CVDs) aredisorders of the heart and blood vessels and are a major cause of disability and premature death worldwide. Individuals at higher risk of developing CVD must be noticed at an early stage to prevent premature deaths. Advances in the field of computational intelligence, together with the vast amount of data produced daily in clinical settings, have made it possible to create recognition systems capable of identifying hidden patterns and useful information. This paper focuses on the application of Data Mining Techniques (DMTs) to clinical data collected during the medical examination in an attempt to predict whether or not an individual has a CVD. To this end, the CRossIndustry Standard Process for Data Mining (CRISP-DM) methodology was followed, in which five classifiers were applied, namely DT, Optimized DT, RI, RF, and DL. The models were mainly developed using the RapidMiner software with the assist of the WEKA tool and were analyzed based on accuracy, precision, sensitivity, and specificity. The results obtained were considered promising on the basis of the research for effective means of diagnosing CVD, with the best model being Optimized DT, which achieved the highest values for all the evaluation metrics, 73.54%, 75.82%, 68.89%, 78.16% and 0.788 for accuracy, precision, sensitivity, specificity, and AUC, respectively. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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