Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Accurate 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.
Açıklama
Anahtar Kelimeler
Cardiovascular Disease, Decision Trees, K-nearest Neighbors, Logistic Regression, Predictions, Support Vector Machines
Kaynak
Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Bai, 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.
