Integrative machine learning approaches for enhanced cardiovascular disease prediction: a comparative analysis of XGBoost and ANFIS algorithms

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dc.contributor.authorMuhyi, Diyar Fadhil
dc.contributor.authorAta, Oğuz
dc.date.accessioned2025-08-15T12:27:22Z
dc.date.available2025-08-15T12:27:22Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalı
dc.description.abstractCardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the urgent need for advanced diagnostic tools to improve early detection and patient outcomes. This study evaluates the predictive performance of two machine learning models-Extreme Gradient Boosting (XGBoost) and the Adaptive Neuro-Fuzzy Inference System (ANFIS)-across five datasets from the UCI Machine Learning Repository: Cleveland, Hungary, Switzerland, Long Beach VA, and Statlog Heart. Comprehensive preprocessing steps-including imputation, standardization, one-hot encoding, and SMOTEENN-were applied to ensure data consistency and address class imbalance. XGBoost achieved perfect accuracy (100%) on the Switzerland and Statlog datasets, reflecting its strength in structured data environments and consistent predictive performance. Conversely, ANFIS outperformed XGBoost on the Cleveland dataset, demonstrating its effectiveness in modeling complex, nonlinear relationships. Performance evaluation metrics included accuracy, precision, recall, F1 score, F2 score, and ROC-AUC. XGBoost consistently delivered high precision and recall, which are essential for minimizing false positives and negatives in clinical settings. ANFIS yielded high F2 scores, indicating a stronger emphasis on reducing false negatives-a critical concern in CVD diagnosis. This comparative analysis suggests that while XGBoost is well suited for scalable, high-throughput diagnostic applications, ANFIS offers greater interpretability and is more effective in nuanced clinical scenarios. These findings underscore the potential of integrating advanced machine learning models into cardiovascular disease prediction frameworks to enhance diagnostic accuracy and support real-world healthcare decision-making.
dc.identifier.citationMuhyi, D. F., & Ata, O. (2025). Integrative machine learning approaches for enhanced cardiovascular disease prediction: a comparative analysis of XGBoost and ANFIS algorithms. The Journal of Supercomputing, 81(12), 1-48. 10.1007/s11227-025-07687-9
dc.identifier.doi10.1007/s11227-025-07687-9
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.issue12
dc.identifier.scopus2-s2.0-105012589817
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5933
dc.identifier.volume81
dc.identifier.wosWOS:001544699500002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakscopus
dc.institutionauthorMuhyi, Diyar Fadhil
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Supercomputing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectANFIS
dc.subjectCardiovascular diseases
dc.subjectDiagnostic predictive modeling
dc.subjectMachine learning
dc.subjectXGBoost
dc.titleIntegrative machine learning approaches for enhanced cardiovascular disease prediction: a comparative analysis of XGBoost and ANFIS algorithms
dc.typeArticle

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