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Enhanced Fuzzy Score-Based Decision Support System for Early Stroke Prediction

2025-01-31Artigo de investigação Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorKahla, Mayssa Ben
dc.contributor.authorKanzari, Dalel
dc.contributor.authorAmor, Sana Ben
dc.contributor.authorGhannouchi, Sonia Ayachi
dc.contributor.authorMartinho, Ricardo
dc.date.accessioned2026-04-27T10:29:04Z
dc.date.available2026-04-27T10:29:04Z
dc.date.issued2025-01-31en_US
dc.date.updated2026-04-23T12:13:42Z
dc.descriptionArticle number - 7
dc.descriptionThe research introduces an innovative approach for early stroke prediction using a fuzzy scoring-based Decision Support System. This approach encompasses three main modules: Mind map-based Data Modeling, Fuzzy scoring computing, and Machine Learning (ML)-Based Decision System. By incorporating fuzzy logic, the approach extracts valuable knowledge from imprecise and uncertain data. Combining the fuzzy stroke risk model with a ML-based decision support system aims to enhance stroke prediction accuracy and improve preventive measures and patient outcomes.
dc.description.abstractAccording to the Global Health Observatory, stroke ranks second worldwide in causing dementia, right after Alzheimer’s disease. The mortality rate linked to dementia resulting from stroke is high because symptoms are often recognized late, and stroke can be misinterpreted as other brain disorders. Early detection and diagnosis of stroke is crucial. Therefore, increasing awareness of stroke symptoms and implementing preventive measures becomes imperative. Prompt intervention by healthcare professionals can improve outcomes and reduce long-term complications of stroke. The research introduces an innovative approach for early stroke prediction using a fuzzy scoring-based Decision Support System. This approach encompasses three main modules: Mind map-based Data Modeling, Fuzzy scoring computing, and Machine Learning (ML)-Based Decision System. By incorporating fuzzy logic, the approach extracts valuable knowledge from imprecise and uncertain data. Combining the fuzzy stroke risk model with a ML-based decision support system aims to enhance stroke prediction accuracy and improve preventive measures and patient outcomes. The approach’s effectiveness was validated using real clinical data and tested with various ML classifiers, including K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The results showed a strong correlation between stroke cases and computed risk-scoring values. In comparison to predictions without fuzzy scoring and other related works, the stroke risk prediction using the proposed approach demonstrated higher accuracy, making it a promising method for early stroke detection and prevention.eng
dc.description.versionN/A
dc.identifier.citationBen Kahla, M., Kanzari, D., Ben Amor, S., Ayachi Ghannouchi, S., & Martinho, R. (2025). Enhanced Fuzzy Score-Based Decision Support System for Early Stroke Prediction. ACM Transactions on Computing for Healthcare, 6(1), 7. https://doi.org/10.1145/3703461
dc.identifier.doi10.1145/3703461en_US
dc.identifier.eissn2637-8051
dc.identifier.slugcv-prod-4273880
dc.identifier.urihttp://hdl.handle.net/10400.8/16196
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.hasversionhttps://dl.acm.org/doi/10.1145/3703461
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectStroke prediction
dc.subjectFuzzy logic
dc.subjectDecision support system
dc.subjectMachine learning
dc.subjectHealthcare analytics
dc.titleEnhanced Fuzzy Score-Based Decision Support System for Early Stroke Predictioneng
dc.typeresearch articleen_US
dspace.entity.typePublication
oaire.citation.endPage23
oaire.citation.issue1
oaire.citation.startPage1
oaire.citation.titleACM Transactions on Computing for Healthcareen_US
oaire.citation.volume6
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMartinho
person.givenNameRicardo
person.identifier.ciencia-idF51E-9BB5-EF92
person.identifier.orcid0000-0003-1157-7510
person.identifier.ridK-8277-2013
person.identifier.scopus-author-id25823103700
rcaap.cv.cienciaidF51E-9BB5-EF92 | Ricardo Martinho
rcaap.rightsopenAccessen_US
relation.isAuthorOfPublicationb2a74e46-f06c-4dcd-8c64-8f78f1d55440
relation.isAuthorOfPublication.latestForDiscoveryb2a74e46-f06c-4dcd-8c64-8f78f1d55440

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