A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases

Norma Latif Fitriyani, Muhammad Syafrudin, Nur Chamidah (), Marisa Rifada, Hendri Susilo, Dursun Aydin, Syifa Latif Qolbiyani and Seung Won Lee ()
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Norma Latif Fitriyani: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Muhammad Syafrudin: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Nur Chamidah: Department of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia
Marisa Rifada: Department of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia
Hendri Susilo: Department of Cardiology and Vascular Medicine, Faculty of Medicine, Airlangga University, Surabaya 60286, Indonesia
Dursun Aydin: Department of Statistics, Faculty of Science, Muğla Sıtkı Koçman University, Muğla 48000, Turkey
Syifa Latif Qolbiyani: Department of Community Development, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Seung Won Lee: Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea

Mathematics, 2025, vol. 13, issue 13, 1-26

Abstract: 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.

Keywords: machine learning; bagging algorithm; histogram gradient boosting; local outlier factor; information gain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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