Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384520063> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4384520063 abstract "Majority of strokes are brought on by an unanticipated obstruction of blood flow to the brain and heart. Stroke severity can be reduced by being aware of the various stroke warning signs in advance. Previous study on stroke prediction had an accuracy less than 90%. Sample size of 1000 – 2000 for that study was insufficient to justify the results obtained by the trained model. In this study, comparisons are made among different approaches to the stroke prediction model, include four different classification methods, which are logistic regression, Random Forest, Decision Tree and Support Vector Machine (SVM). The results obtained by the classifiers were trained with 2000 samples and 3109. All the classifiers were then tested individually. The accuracy for each model are, 91% for Decision Tree, 95% for Random Forest, 95% for Logistic Regression and 100% Support Vector Machine (SVM). As a conclusion, our study suggested that SVM approach is fit well for stroke prediction model as it achieved the highest accuracy compared to the others." @default.
- W4384520063 created "2023-07-18" @default.
- W4384520063 creator A5001786914 @default.
- W4384520063 creator A5006081758 @default.
- W4384520063 creator A5085401911 @default.
- W4384520063 creator A5092484414 @default.
- W4384520063 date "2022-09-23" @default.
- W4384520063 modified "2023-09-27" @default.
- W4384520063 title "Stroke Prediction Model Using Machine Learning Method" @default.
- W4384520063 cites W1954775015 @default.
- W4384520063 cites W1988225139 @default.
- W4384520063 cites W2094266213 @default.
- W4384520063 cites W2273969070 @default.
- W4384520063 cites W2409558506 @default.
- W4384520063 cites W2520062369 @default.
- W4384520063 cites W2766670955 @default.
- W4384520063 cites W2783677152 @default.
- W4384520063 cites W2783844319 @default.
- W4384520063 cites W2791595050 @default.
- W4384520063 cites W2896821547 @default.
- W4384520063 cites W2911913928 @default.
- W4384520063 cites W2946916415 @default.
- W4384520063 cites W2992806896 @default.
- W4384520063 cites W3004022591 @default.
- W4384520063 cites W3014360014 @default.
- W4384520063 cites W3091780972 @default.
- W4384520063 doi "https://doi.org/10.1109/iche55634.2022.10179868" @default.
- W4384520063 hasPublicationYear "2022" @default.
- W4384520063 type Work @default.
- W4384520063 citedByCount "0" @default.
- W4384520063 crossrefType "proceedings-article" @default.
- W4384520063 hasAuthorship W4384520063A5001786914 @default.
- W4384520063 hasAuthorship W4384520063A5006081758 @default.
- W4384520063 hasAuthorship W4384520063A5085401911 @default.
- W4384520063 hasAuthorship W4384520063A5092484414 @default.
- W4384520063 hasConcept C119857082 @default.
- W4384520063 hasConcept C12267149 @default.
- W4384520063 hasConcept C127413603 @default.
- W4384520063 hasConcept C151956035 @default.
- W4384520063 hasConcept C153180895 @default.
- W4384520063 hasConcept C154945302 @default.
- W4384520063 hasConcept C169258074 @default.
- W4384520063 hasConcept C2780645631 @default.
- W4384520063 hasConcept C41008148 @default.
- W4384520063 hasConcept C45804977 @default.
- W4384520063 hasConcept C78519656 @default.
- W4384520063 hasConcept C84525736 @default.
- W4384520063 hasConceptScore W4384520063C119857082 @default.
- W4384520063 hasConceptScore W4384520063C12267149 @default.
- W4384520063 hasConceptScore W4384520063C127413603 @default.
- W4384520063 hasConceptScore W4384520063C151956035 @default.
- W4384520063 hasConceptScore W4384520063C153180895 @default.
- W4384520063 hasConceptScore W4384520063C154945302 @default.
- W4384520063 hasConceptScore W4384520063C169258074 @default.
- W4384520063 hasConceptScore W4384520063C2780645631 @default.
- W4384520063 hasConceptScore W4384520063C41008148 @default.
- W4384520063 hasConceptScore W4384520063C45804977 @default.
- W4384520063 hasConceptScore W4384520063C78519656 @default.
- W4384520063 hasConceptScore W4384520063C84525736 @default.
- W4384520063 hasLocation W43845200631 @default.
- W4384520063 hasOpenAccess W4384520063 @default.
- W4384520063 hasPrimaryLocation W43845200631 @default.
- W4384520063 hasRelatedWork W3127425528 @default.
- W4384520063 hasRelatedWork W3143658565 @default.
- W4384520063 hasRelatedWork W4246246790 @default.
- W4384520063 hasRelatedWork W4281846282 @default.
- W4384520063 hasRelatedWork W4293191462 @default.
- W4384520063 hasRelatedWork W4312707991 @default.
- W4384520063 hasRelatedWork W4321636153 @default.
- W4384520063 hasRelatedWork W4322731370 @default.
- W4384520063 hasRelatedWork W4383535405 @default.
- W4384520063 hasRelatedWork W4384520063 @default.
- W4384520063 isParatext "false" @default.
- W4384520063 isRetracted "false" @default.
- W4384520063 workType "article" @default.