Optimizing Cardiovascular Disease Risk Prediction Using Ensemble Learning Techniques
Resumo
The heart is one of the most essential organs, responsible for ensuring blood circulation throughout the human body. A wide range of cardiovascular diseases (CVDs) poses severe risks to human health. According to data from the World Health Organization (WHO), CVDs account for approximately 17.9 million deaths annually, representing 31% of all global fatalities. Machine learning techniques have demonstrated significant potential in effectively predicting the risk of CVD occurrence. In this study, ensemble learning algorithms were employed to optimize predictive analysis for cardiovascular events. The dataset Indicators of Heart Disease (2022 UPDATE), provided by the Centers for Disease Control and Prevention (CDC), served as the basis for model training and evaluation. The best-performing models were Gradient Boosting and Logistic Regression, both achieving an accuracy and precision rate of 94.9%.
Palavras-chave:
Cardiovascular Diseases, Machine Learning, Predictive Analysis, Gradient Boosting
Referências
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I. Ibrahim and A. Abdulazeez, “The role of machine learning algorithms for diagnosing diseases,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 10–19, 2021.
T. N. Rincy and R. Gupta, “Ensemble learning techniques and its efficiency in machine learning: A survey,” in 2nd International Conference on Data, Engineering and Applications (IDEA), 2020, pp. 1–6.
P. Mahajan, S. Uddin, F. Hajati, and M. A. Moni, “Ensemble learning for disease prediction: A review,” Healthcare, vol. 11, no. 12, 2023. [Online]. Available: [link]
A. Vinora, E. Lloyds, R. Nancy Deborah, M. Anandha Surya, V. Krithik Deivarajan, and M. MuthuVignesh, “Heart disease prediction using ensemble model,” in 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1), 2023, pp. 1–6.
A. Ramachandran, S. K. Burra, and S. S. Verma, “Early cardiovascular disease detection using ensemble methods,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), vol. 6, 2023, pp. 2373–2379.
K. P. Joshi, M. L. Prasad, R. Natchadalingam, P. C. S. Reddy, S. Mukherjee, and G. C. Babu, “An accurate prediction of coronary heart disease using ensemble algorithms,” in 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), 2023, pp. 1–5.
V. K. HM and D. Suresh, “Performance analysis of base and meta classifiers and the prediction of cardiovascular disease using ensemble stacking,” in 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2023, pp. 584–589.
R. Mia, S. Khanam, A. Mahjabeen, N. H. Ovy, D. Ghimire, M.-J. Park, M. I. A. Begum, and A. S. M. S. Hosen, “Exploring machine learning for predicting cerebral stroke: A study in discovery,” Electronics, vol. 13, no. 4, 2024. [Online]. Available: [link]
X.-Y. Gao, A. Amin Ali, H. Shaban Hassan, and E. M. Anwar, “Improving the accuracy for analyzing heart diseases prediction based on the ensemble method,” Complexity, vol. 2021, pp. 1–10, 2021.
B. S. K. Jayasudha, P. N. Sudha, K. Keshav, and N. Ramesh, “Ensemble learning as a prerogative method of predicting mortality of patients with cardiovascular diseases,” in 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2021, pp. 01–05.
Q. Ye, L. Qiao, H. Chen, Q. Tao, and J. Xiao, “Automatic cardiomyopathy diagnosis with a cost-sensitive ensemble classifier,” in 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT), 2021, pp. 775–779.
D. R. S. Victoria, K. Mayuri, N. Ramalakshmi, and M. G. Kumar, “An ensemble stacking methodology for identifying heart disease,” in 2023 4th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2023, pp. 1139–1143.
T. Mahmud, A. Barua, M. Begum, E. Chakma, S. Das, and N. Sharmen, “An improved framework for reliable cardiovascular disease prediction using hybrid ensemble learning,” in 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2023, pp. 1–6.
S. Ambade and D. Chikmurge, “Enhancing cardiovascular disease prediction using ensemble learning,” in 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE, 2023, pp. 1–6.
A. Ashfaq, A. Imran, I. Ullah, A. Alzahrani, K. M. A. Alheeti, and A. Yasin, “Multi-model ensemble based approach for heart disease diagnosis,” in 2022 International Conference on Recent Advances in Electrical Engineering Computer Sciences (RAEE CS), 2022, pp. 1–8.
Publicado
22/10/2025
Como Citar
SANTOS, Abner Gabriel Dias dos; MARCOLINO, Bruno Eduardo; LUCAS, Thiago José; LOPES, Alessandra de Souza; TOJEIRO, Carlos Alexandre Carvalho; COSTA, Kelton Augusto Pontara da.
Optimizing Cardiovascular Disease Risk Prediction Using Ensemble Learning Techniques. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 22. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 347-353.
DOI: https://doi.org/10.5753/latinoware.2025.16443.
