Brain stroke prediction using stacked ensemble model
Stroke is a potentially fatal illness that requires emergency care. There is a greater chance that the patient will recover and resume their regular life when they receive treatment and diagnosis as soon as feasible. Artificial Intelligence has the potential to significantly impact stroke diagnosis...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/25591/ http://journalarticle.ukm.my/25591/1/kejut_38.pdf |
| _version_ | 1848816399205531648 |
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| author | Hemalatha Gunasekaran, Angelin Gladys, Deepa Kanmani, Rex Macedo, Wilfred Blessing N R, |
| author_facet | Hemalatha Gunasekaran, Angelin Gladys, Deepa Kanmani, Rex Macedo, Wilfred Blessing N R, |
| author_sort | Hemalatha Gunasekaran, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Stroke is a potentially fatal illness that requires emergency care. There is a greater chance that the patient will recover and resume their regular life when they receive treatment and diagnosis as soon as feasible. Artificial Intelligence has the potential to significantly impact stroke diagnosis and facilitate prompt patient treatment for physicians. Machine learning can be utilized in stroke prediction by evaluating huge volumes of patient data and detecting patterns and risk variables that may contribute to the likelihood of a stroke. In this study, we explored a stacked ensemble model that uses four base models—Decision Tree, XGBoost, RandomForest, and ExtraTree classifiers to predict the stroke. We discovered that the accuracy of the stacked ensemble model was 96.35%, higher than that of the traditional machine-learning models, other ensemble models, and ANN model. |
| first_indexed | 2025-11-15T01:05:15Z |
| format | Article |
| id | oai:generic.eprints.org:25591 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:05:15Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:255912025-07-14T08:55:28Z http://journalarticle.ukm.my/25591/ Brain stroke prediction using stacked ensemble model Hemalatha Gunasekaran, Angelin Gladys, Deepa Kanmani, Rex Macedo, Wilfred Blessing N R, Stroke is a potentially fatal illness that requires emergency care. There is a greater chance that the patient will recover and resume their regular life when they receive treatment and diagnosis as soon as feasible. Artificial Intelligence has the potential to significantly impact stroke diagnosis and facilitate prompt patient treatment for physicians. Machine learning can be utilized in stroke prediction by evaluating huge volumes of patient data and detecting patterns and risk variables that may contribute to the likelihood of a stroke. In this study, we explored a stacked ensemble model that uses four base models—Decision Tree, XGBoost, RandomForest, and ExtraTree classifiers to predict the stroke. We discovered that the accuracy of the stacked ensemble model was 96.35%, higher than that of the traditional machine-learning models, other ensemble models, and ANN model. Penerbit Universiti Kebangsaan Malaysia 2024-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25591/1/kejut_38.pdf Hemalatha Gunasekaran, and Angelin Gladys, and Deepa Kanmani, and Rex Macedo, and Wilfred Blessing N R, (2024) Brain stroke prediction using stacked ensemble model. Jurnal Kejuruteraan, 36 (4). pp. 1759-1768. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3604-2024/ |
| spellingShingle | Hemalatha Gunasekaran, Angelin Gladys, Deepa Kanmani, Rex Macedo, Wilfred Blessing N R, Brain stroke prediction using stacked ensemble model |
| title | Brain stroke prediction using stacked ensemble model |
| title_full | Brain stroke prediction using stacked ensemble model |
| title_fullStr | Brain stroke prediction using stacked ensemble model |
| title_full_unstemmed | Brain stroke prediction using stacked ensemble model |
| title_short | Brain stroke prediction using stacked ensemble model |
| title_sort | brain stroke prediction using stacked ensemble model |
| url | http://journalarticle.ukm.my/25591/ http://journalarticle.ukm.my/25591/ http://journalarticle.ukm.my/25591/1/kejut_38.pdf |