Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test.
Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The alg...
| Publicado en: | Malaysian Journal of Medical Sciences Vol. 28; no. 5; pp. 118 - 130 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Malaysian Journal of Medical Sciences
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=153366410&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153366410 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1394195X YQP jtl: Malaysian Journal of Medical Sciences issn: 1394195X maglogo: N pubinfo: dt: 2021 vid: 28 iid: 5 pid: 23493 pub: Malaysian Journal of Medical Sciences artinfo: ui: 153366410 153366410 153366410 10.21315/mjms2021.28.5.12 153366410 ppf: 118 ppct: 12 formats: tig: atl: Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test. aug: au: BANJOKO, ALABI WAHEED ABDULAZEEZ, KAWTHAR OPEYEMI affil: Department of Statistics, Faculty of Physical Sciences, University of Ilorin, Ilorin, Kwara State, Nigeria sug: subj: Data Mining Algorithms Heart Diseases Classification Heart Diseases Prognosis Angiography Human Support Vector Machine Systems Analysis Models, Statistical Comparative Studies Machine Learning Methods ab: Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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