Matches in SemOpenAlex for { <https://semopenalex.org/work/W3116385418> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W3116385418 abstract "Most of strokes will occur due to an unexpected obstruction of courses by prompting both the brain and heart. Early awareness for different warning signs of stroke can minimize the stroke. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average glucose level, smoking status, previous stroke and age. Using these high features attributes, ten different classifiers have been trained, they are Logistics Regression, Stochastic Gradient Descent, Decision Tree Classifier, AdaBoost Classifier, Gaussian Classifier, Quadratic Discriminant Analysis, Multi layer Perceptron Classifier, KNeighbors Classifier, Gradient Boosting Classifier, and XGBoost Classifier for predicting the stroke. Afterwards, results of the base classifiers are aggregated by using the weighted voting approach to reach highest accuracy. Moreover, the proposed study has achieved an accuracy of 97%, where the weighted voting classifier performs better than the base classifiers. This model gives the best accuracy for the stroke prediction. The area under curve value of weighted voting classifier is also high. False positive rate and false negative rate of weighted classifier is lowest compared with others. As a result, weighted voting is almost the perfect classifier for predicting the stroke that can be used by physicians and patients to prescribe and early detect a potential stroke." @default.
- W3116385418 created "2021-01-05" @default.
- W3116385418 creator A5017209539 @default.
- W3116385418 creator A5055375837 @default.
- W3116385418 creator A5057247142 @default.
- W3116385418 creator A5065253317 @default.
- W3116385418 creator A5066116412 @default.
- W3116385418 creator A5087150727 @default.
- W3116385418 date "2020-11-05" @default.
- W3116385418 modified "2023-10-10" @default.
- W3116385418 title "Performance Analysis of Machine Learning Approaches in Stroke Prediction" @default.
- W3116385418 cites W1954775015 @default.
- W3116385418 cites W1988225139 @default.
- W3116385418 cites W2094266213 @default.
- W3116385418 cites W2273969070 @default.
- W3116385418 cites W2409558506 @default.
- W3116385418 cites W2520062369 @default.
- W3116385418 cites W2766670955 @default.
- W3116385418 cites W2768956845 @default.
- W3116385418 cites W2783677152 @default.
- W3116385418 cites W2783844319 @default.
- W3116385418 cites W2791595050 @default.
- W3116385418 cites W2896821547 @default.
- W3116385418 cites W2911913928 @default.
- W3116385418 cites W2946916415 @default.
- W3116385418 cites W2992806896 @default.
- W3116385418 cites W3004022591 @default.
- W3116385418 cites W3014360014 @default.
- W3116385418 cites W3091780972 @default.
- W3116385418 doi "https://doi.org/10.1109/iceca49313.2020.9297525" @default.
- W3116385418 hasPublicationYear "2020" @default.
- W3116385418 type Work @default.
- W3116385418 sameAs 3116385418 @default.
- W3116385418 citedByCount "45" @default.
- W3116385418 countsByYear W31163854182021 @default.
- W3116385418 countsByYear W31163854182022 @default.
- W3116385418 countsByYear W31163854182023 @default.
- W3116385418 crossrefType "proceedings-article" @default.
- W3116385418 hasAuthorship W3116385418A5017209539 @default.
- W3116385418 hasAuthorship W3116385418A5055375837 @default.
- W3116385418 hasAuthorship W3116385418A5057247142 @default.
- W3116385418 hasAuthorship W3116385418A5065253317 @default.
- W3116385418 hasAuthorship W3116385418A5066116412 @default.
- W3116385418 hasAuthorship W3116385418A5087150727 @default.
- W3116385418 hasConcept C119857082 @default.
- W3116385418 hasConcept C141404830 @default.
- W3116385418 hasConcept C151956035 @default.
- W3116385418 hasConcept C153180895 @default.
- W3116385418 hasConcept C154945302 @default.
- W3116385418 hasConcept C169258074 @default.
- W3116385418 hasConcept C41008148 @default.
- W3116385418 hasConcept C52620605 @default.
- W3116385418 hasConcept C95623464 @default.
- W3116385418 hasConceptScore W3116385418C119857082 @default.
- W3116385418 hasConceptScore W3116385418C141404830 @default.
- W3116385418 hasConceptScore W3116385418C151956035 @default.
- W3116385418 hasConceptScore W3116385418C153180895 @default.
- W3116385418 hasConceptScore W3116385418C154945302 @default.
- W3116385418 hasConceptScore W3116385418C169258074 @default.
- W3116385418 hasConceptScore W3116385418C41008148 @default.
- W3116385418 hasConceptScore W3116385418C52620605 @default.
- W3116385418 hasConceptScore W3116385418C95623464 @default.
- W3116385418 hasLocation W31163854181 @default.
- W3116385418 hasOpenAccess W3116385418 @default.
- W3116385418 hasPrimaryLocation W31163854181 @default.
- W3116385418 hasRelatedWork W3146991051 @default.
- W3116385418 hasRelatedWork W3153080799 @default.
- W3116385418 hasRelatedWork W4226239449 @default.
- W3116385418 hasRelatedWork W4242609709 @default.
- W3116385418 hasRelatedWork W4249229055 @default.
- W3116385418 hasRelatedWork W4281616679 @default.
- W3116385418 hasRelatedWork W4283313480 @default.
- W3116385418 hasRelatedWork W4319430317 @default.
- W3116385418 hasRelatedWork W4322744035 @default.
- W3116385418 hasRelatedWork W4375930479 @default.
- W3116385418 isParatext "false" @default.
- W3116385418 isRetracted "false" @default.
- W3116385418 magId "3116385418" @default.
- W3116385418 workType "article" @default.