Matches in SemOpenAlex for { <https://semopenalex.org/work/W2954788759> ?p ?o ?g. }
Showing items 1 to 62 of
62
with 100 items per page.
- W2954788759 endingPage "100203" @default.
- W2954788759 startingPage "100203" @default.
- W2954788759 abstract "Abstract Machine learning involves artificial intelligence, and it is used in solving many problems in data science. One common application of machine learning is the prediction of an outcome based upon existing data. The machine learns patterns from the existing dataset, and then applies them to an unknown dataset in order to predict the outcome. Classification is a powerful machine learning technique that is commonly used for prediction. Some classification algorithms predict with satisfactory accuracy, whereas others exhibit a limited accuracy. This paper investigates a method termed ensemble classification, which is used for improving the accuracy of weak algorithms by combining multiple classifiers. Experiments with this tool were performed using a heart disease dataset. A comparative analytical approach was done to determine how the ensemble technique can be applied for improving prediction accuracy in heart disease. The focus of this paper is not only on increasing the accuracy of weak classification algorithms, but also on the implementation of the algorithm with a medical dataset, to show its utility to predict disease at an early stage. The results of the study indicate that ensemble techniques, such as bagging and boosting, are effective in improving the prediction accuracy of weak classifiers, and exhibit satisfactory performance in identifying risk of heart disease. A maximum increase of 7% accuracy for weak classifiers was achieved with the help of ensemble classification. The performance of the process was further enhanced with a feature selection implementation, and the results showed significant improvement in prediction accuracy." @default.
- W2954788759 created "2019-07-12" @default.
- W2954788759 creator A5010389639 @default.
- W2954788759 creator A5044358344 @default.
- W2954788759 date "2019-01-01" @default.
- W2954788759 modified "2023-10-11" @default.
- W2954788759 title "Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques" @default.
- W2954788759 cites W1850308234 @default.
- W2954788759 cites W2068677783 @default.
- W2954788759 cites W2411631905 @default.
- W2954788759 cites W2900794383 @default.
- W2954788759 cites W4240238604 @default.
- W2954788759 doi "https://doi.org/10.1016/j.imu.2019.100203" @default.
- W2954788759 hasPublicationYear "2019" @default.
- W2954788759 type Work @default.
- W2954788759 sameAs 2954788759 @default.
- W2954788759 citedByCount "254" @default.
- W2954788759 countsByYear W29547887592020 @default.
- W2954788759 countsByYear W29547887592021 @default.
- W2954788759 countsByYear W29547887592022 @default.
- W2954788759 countsByYear W29547887592023 @default.
- W2954788759 crossrefType "journal-article" @default.
- W2954788759 hasAuthorship W2954788759A5010389639 @default.
- W2954788759 hasAuthorship W2954788759A5044358344 @default.
- W2954788759 hasBestOaLocation W29547887591 @default.
- W2954788759 hasConcept C119857082 @default.
- W2954788759 hasConcept C126322002 @default.
- W2954788759 hasConcept C153180895 @default.
- W2954788759 hasConcept C154945302 @default.
- W2954788759 hasConcept C2779134260 @default.
- W2954788759 hasConcept C41008148 @default.
- W2954788759 hasConcept C45942800 @default.
- W2954788759 hasConcept C71924100 @default.
- W2954788759 hasConceptScore W2954788759C119857082 @default.
- W2954788759 hasConceptScore W2954788759C126322002 @default.
- W2954788759 hasConceptScore W2954788759C153180895 @default.
- W2954788759 hasConceptScore W2954788759C154945302 @default.
- W2954788759 hasConceptScore W2954788759C2779134260 @default.
- W2954788759 hasConceptScore W2954788759C41008148 @default.
- W2954788759 hasConceptScore W2954788759C45942800 @default.
- W2954788759 hasConceptScore W2954788759C71924100 @default.
- W2954788759 hasLocation W29547887591 @default.
- W2954788759 hasLocation W29547887592 @default.
- W2954788759 hasOpenAccess W2954788759 @default.
- W2954788759 hasPrimaryLocation W29547887591 @default.
- W2954788759 hasRelatedWork W3013699712 @default.
- W2954788759 hasRelatedWork W4200126462 @default.
- W2954788759 hasRelatedWork W4200409985 @default.
- W2954788759 hasRelatedWork W4281757034 @default.
- W2954788759 hasRelatedWork W4285046548 @default.
- W2954788759 hasRelatedWork W4285741730 @default.
- W2954788759 hasRelatedWork W4292969247 @default.
- W2954788759 hasRelatedWork W4311847748 @default.
- W2954788759 hasRelatedWork W4312241010 @default.
- W2954788759 hasRelatedWork W4313488044 @default.
- W2954788759 hasVolume "16" @default.
- W2954788759 isParatext "false" @default.
- W2954788759 isRetracted "false" @default.
- W2954788759 magId "2954788759" @default.
- W2954788759 workType "article" @default.