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A Gradient Boosting–Based Platform with Fuzzy Linguistic Representation for Cardiovascular Disease Risk Prediction
Corresponding Author(s) : Amir Saleh
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 3, August 2026
Abstract
Cardiovascular disease (CVD) remains one of the leading causes of mortality globally, thus early risk prediction critical for preventative healthcare. Although machine learning methods have shown promising results in CVD prediction, many existing models are difficult to interpret in clinical practice. This study proposes a CVD risk prediction platform that combines gradient boosting (GB) with fuzzy linguistic representation to improve both predictive performance and interpretability. approach has numerous preprocessing phases, including data cleaning, normalization, outlier handling, and recursive feature elimination (RFE) for feature selection. Numerical clinical attributes are transformed into fuzzy linguistic variables to provide more intuitive risk interpretation for healthcare professionals. The gradient boosting model is trained using both original and fuzzy-transformed feature representations to improve model generalization. The proposed approach is evaluated against several baseline machine learning models using accuracy, precision, recall, and F1-score. Experimental results show that the proposed framework achieves better performance than conventional models, with a maximum accuracy of 94.30%. In addition, the developed platform provides visual risk interpretation and decision-support insights that may assist healthcare practitioners in evaluating cardiovascular risk more effectively.
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References
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F. García-Peñalvo et al., “KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals,” Int. J. Interact. Multimed. Artif. Intell., vol. In Press, no. In Press, p. 1, 2023. https://doi.org/10.9781/ijimai.2023.01.006
Y. Liu, Z. Ling, B. Huo, B. Wang, T. Chen, and E. Mouine, “Building A Platform for Machine Learning Operations from Open Source Frameworks,” IFAC-PapersOnLine, vol. 53, no. 5, pp. 704–709, 2020. https://doi.org/10.1016/j.ifacol.2021.04.161
G. Quer, R. Arnaout, M. Henne, and R. Arnaout, “Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review,” J. Am. Coll. Cardiol., vol. 77, no. 3, pp. 300–313, 2021. https://doi.org/10.1016/j.jacc.2020.11.030
S. Zeadally, F. Siddiqui, Z. Baig, and A. Ibrahim, “Smart healthcare: Challenges and potential solutions using internet of things (IoT) and big data analytics,” PSU Res. Rev., vol. 4, no. 2, pp. 149–168, 2020. https://doi.org/10.1108/PRR-08-2019-0027
S. Nashif, M. R. Raihan, M. R. Islam, and M. H. Imam, “Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System,” World J. Eng. Technol., vol. 06, no. 04, pp. 854–873, 2018. https://doi.org/10.4236/wjet.2018.64057
O. Faust, N. Lei, E. Chew, E. J. Ciaccio, and U. R. Acharya, “A smart service platform for cost efficient cardiac health monitoring,” Int. J. Environ. Res. Public Health, vol. 17, no. 17, pp. 1–18, 2020. https://doi.org/10.3390/ijerph17176313
C. A. Gómez-García, M. Askar-Rodriguez, and J. Velasco-Medina, “Platform for Healthcare Promotion and Cardiovascular Disease Prevention,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 7, pp. 2758–2767, 2021. https://doi.org/10.1109/JBHI.2021.3051967
A. Damayunita, R. S. Fuadi, and C. Juliane, “Comparative Analysis of Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) Algorithms for Classification of Heart Disease Patients,” J. Online Inform., vol. 7, no. 2, pp. 219–225, 2022. https://doi.org/10.15575/join.v7i2.919
R. Waigi, S. Choudhary, P. Fulzele, and G. Mishra, “Predicting The Risk Of Heart Disease Using Advanced Machine Learning Approach,” Eur. J. Mol. Clin. Med., vol. 7, no. May, p. 2020, 2020.
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J. B. Jane and E. N. Ganesh, “A review on big data with machine learning and fuzzy logic for better decision making,” Int. J. Sci. Technol. Res., vol. 8, no. 10, pp. 1221–1225, 2019.
L. Maretto, M. Faccio, and D. Battini, “A Multi-Criteria Decision-Making Model Based on Fuzzy Logic and AHP for the Selection of Digital Technologies,” IFAC-PapersOnLine, vol. 55, no. 2, pp. 319–324, 2022. https://doi.org/10.1016/j.ifacol.2022.04.213
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K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, 2022. https://doi.org/10.1016/j.gltp.2022.04.020
H. Lattar, A. Ben Salem, and H. H. Ben Ghezala, “Does data cleaning improve heart disease prediction?,” Procedia Comput. Sci., vol. 176, pp. 1131–1140, 2020. https://doi.org/10.1016/j.procs.2020.09.109
L. Fanani and N. Priandani, “Data Cleaning and Prototyping Using K-Means to Enhance Classification Accuracy,” Int. J. Appl. Eng. Res., vol. 12, pp. 5242–5247, Jan. 2017.
H. A. Prihanditya, “The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease,” J. Soft Comput. Explor., vol. 1, no. 1, pp. 63–69, 2020. https://doi.org/10.52465/joscex.v1i1.8
C. Kuzudisli, B. Bakir-Gungor, N. Bulut, B. Qaqish, and M. Yousef, “Review of feature selection approaches based on grouping of features,” PeerJ, vol. 11, 2023. https://doi.org/10.7717/peerj.15666
H. Jeon and S. Oh, “Hybrid-recursive feature elimination for efficient feature selection,” Appl. Sci., vol. 10, no. 9, pp. 1–9, 2020. https://doi.org/10.3390/app10093211
R. Suhendra et al., “Cardiovascular Disease Prediction Using Gradient Boosting Classifier,” Infolitika J. Data Sci., vol. 1, no. 2, pp. 56–62, 2023. https://doi.org/10.60084/ijds.v1i2.131
S. P. Nainggolan and A. Sinaga, “Comparative Analysis of Accuracy of Random Forest and Gradient Boosting Classifier Algorithm for Diabetes Classification,” Sebatik, vol. 27, no. 1, pp. 97–102, 2023. https://doi.org/10.46984/sebatik.v27i1.2157
S. A. Hicks et al., “On evaluation metrics for medical applications of artificial intelligence,” Sci. Rep., vol. 12, no. 1, pp. 1–9, 2022. https://doi.org/10.1038/s41598-022-09954-8
V. Sharma and S. Singh Samant, “Health Recommendation System by Using Deep Learning and Fuzzy Technique,” SSRN Electron. J., no. Aece, pp. 72–78, 2022. https://doi.org/10.2139/ssrn.4157328
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