Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis

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dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 57810685700
dc.authorwosidFemilda Josephin Joseph Shobana Bai / AGG-4255-2022
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.contributor.authorAshok Kumar, Saranya
dc.contributor.authorMaheswari M.
dc.contributor.authorAruna S. b
dc.contributor.authorKrishnan, Aditya
dc.contributor.authorMajid, Amaan
dc.date.accessioned2025-04-18T09:31:35Z
dc.date.available2025-04-18T09:31:35Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAccurate and early diagnosis of cardiovascular disease is a big concern to improve the patient's well-being. The proposed research is focused toward the prediction of cardiovascular disease using a diversified dataset, which includes the patient's health history and diagnostic test results. The study focuses mainly on data visualization, feature selection, and predictive modeling. To identify the distribution of the features and the relationship between the features, data visualization was performed by using various plots and graphs. The important features in the dataset that can be helpful for better prediction are selected using embedded-based feature selection approach. The prediction of disease utilized machine learning (ML) techniques, including logistic regression (LR), decision trees (DTs), support vector machines (SVMs), and k-nearest neighbors (KNNs). F-1 score, precision, recall, accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves are useful metrics for assessing how well machine learning models perform at predicting disease. These metrics provide insights into the advantages and drawbacks of the models, helping researchers to understand their effectiveness and suitability for specific tasks. 0.98, 0.82, 0.80, and 0.78 are the accuracies, and 0.99, 0.94, 0.89, and 0.83 are the area under the ROC curve (AUC) values of DT, KNN, SVM, and LR predictive models, respectively. The findings provide an insight to the healthcare professionals and researchers to understand the usefulness of predictive modeling for early predictions of cardiovascular disease. © 2024 Elsevier Inc. All rights reserved.
dc.identifier.citationBai, F. J. J. S., Kumar, S. A., Maheswari, M., Aruna, S., Krishnan, A., & Majid, A. (2024). Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis. In Decision-Making Models (pp. 505-521). Academic Press.
dc.identifier.doi10.1016/B978-0-443-16147-6.00013-X
dc.identifier.endpage521
dc.identifier.isbn978-044316147-6, 978-044316148-3
dc.identifier.scopus2-s2.0-85202879948
dc.identifier.scopusqualityN/A
dc.identifier.startpage505
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6817
dc.indekslendigikaynakScopus
dc.institutionauthorBai, Femilda Josephin Joseph Shobana
dc.institutionauthoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofDecision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCardiovascular Disease
dc.subjectDecision Trees
dc.subjectK-nearest Neighbors
dc.subjectLogistic Regression
dc.subjectPredictions
dc.subjectSupport Vector Machines
dc.titleEarly detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis
dc.typeArticle

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