A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases
Cardiovascular diseases (CVDs) rank among the leading global causes of mortality, underscoring the necessity for early detection and effective management. This research presents a novel prediction model for CVDs utilizing a bagging algorithm that incorporates histogram gradient boosting as the estim...
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MDPI AG
2025-07-01
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| author | Norma Latif Fitriyani Muhammad Syafrudin Nur Chamidah Marisa Rifada Hendri Susilo Dursun Aydin Syifa Latif Qolbiyani Seung Won Lee |
| author_facet | Norma Latif Fitriyani Muhammad Syafrudin Nur Chamidah Marisa Rifada Hendri Susilo Dursun Aydin Syifa Latif Qolbiyani Seung Won Lee |
| author_sort | Norma Latif Fitriyani |
| collection | DOAJ |
| description | Cardiovascular diseases (CVDs) rank among the leading global causes of mortality, underscoring the necessity for early detection and effective management. This research presents a novel prediction model for CVDs utilizing a bagging algorithm that incorporates histogram gradient boosting as the estimator. This study leverages three preprocessed cardiovascular datasets, employing the Local Outlier Factor technique for outlier removal and the information gain method for feature selection. Through rigorous experimentation, the proposed model demonstrates superior performance compared to conventional machine learning approaches, such as Logistic Regression, Support Vector Classification, Gaussian Naïve Bayes, Multi-Layer Perceptron, k-nearest neighbors, Random Forest, AdaBoost, gradient boosting, and histogram gradient boosting. Evaluation metrics, including precision, recall, F1 score, accuracy, and AUC, yielded impressive results: 93.90%, 98.83%, 96.30%, 96.25%, and 0.9916 for dataset I; 94.17%, 99.05%, 96.54%, 96.48%, and 0.9931 for dataset II; and 89.81%, 82.40%, 85.91%, 86.66%, and 0.9274 for dataset III. The findings indicate that the proposed prediction model has the potential to facilitate early CVD detection, thereby enhancing preventive strategies and improving patient outcomes. |
| format | Article |
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| institution | Matheson Library |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-d5e10fec2e2c4291b1dec0f42bfd9b7c2025-07-11T14:40:42ZengMDPI AGMathematics2227-73902025-07-011313219410.3390/math13132194A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular DiseasesNorma Latif Fitriyani0Muhammad Syafrudin1Nur Chamidah2Marisa Rifada3Hendri Susilo4Dursun Aydin5Syifa Latif Qolbiyani6Seung Won Lee7Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of KoreaDepartment of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of KoreaDepartment of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, IndonesiaDepartment of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, IndonesiaDepartment of Cardiology and Vascular Medicine, Faculty of Medicine, Airlangga University, Surabaya 60286, IndonesiaDepartment of Statistics, Faculty of Science, Muğla Sıtkı Koçman University, Muğla 48000, TurkeyDepartment of Community Development, Universitas Sebelas Maret, Surakarta 57126, IndonesiaDepartment of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of KoreaCardiovascular diseases (CVDs) rank among the leading global causes of mortality, underscoring the necessity for early detection and effective management. This research presents a novel prediction model for CVDs utilizing a bagging algorithm that incorporates histogram gradient boosting as the estimator. This study leverages three preprocessed cardiovascular datasets, employing the Local Outlier Factor technique for outlier removal and the information gain method for feature selection. Through rigorous experimentation, the proposed model demonstrates superior performance compared to conventional machine learning approaches, such as Logistic Regression, Support Vector Classification, Gaussian Naïve Bayes, Multi-Layer Perceptron, k-nearest neighbors, Random Forest, AdaBoost, gradient boosting, and histogram gradient boosting. Evaluation metrics, including precision, recall, F1 score, accuracy, and AUC, yielded impressive results: 93.90%, 98.83%, 96.30%, 96.25%, and 0.9916 for dataset I; 94.17%, 99.05%, 96.54%, 96.48%, and 0.9931 for dataset II; and 89.81%, 82.40%, 85.91%, 86.66%, and 0.9274 for dataset III. The findings indicate that the proposed prediction model has the potential to facilitate early CVD detection, thereby enhancing preventive strategies and improving patient outcomes.https://www.mdpi.com/2227-7390/13/13/2194machine learningbagging algorithmhistogram gradient boostinglocal outlier factorinformation gain |
| spellingShingle | Norma Latif Fitriyani Muhammad Syafrudin Nur Chamidah Marisa Rifada Hendri Susilo Dursun Aydin Syifa Latif Qolbiyani Seung Won Lee A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases Mathematics machine learning bagging algorithm histogram gradient boosting local outlier factor information gain |
| title | A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases |
| title_full | A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases |
| title_fullStr | A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases |
| title_full_unstemmed | A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases |
| title_short | A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases |
| title_sort | novel approach utilizing bagging histogram gradient boosting and advanced feature selection for predicting the onset of cardiovascular diseases |
| topic | machine learning bagging algorithm histogram gradient boosting local outlier factor information gain |
| url | https://www.mdpi.com/2227-7390/13/13/2194 |
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