A Comparative Performance Analysis of Machine Learning Models for Cardiovascular Disease Prediction Using Hyper Parameter Tuning

Author: Ms. Maitri Bhavsar,Dr. Manish Patel

Published Online: July 1, 2026

DOI: http://doi.org/10.63766/spujstmr.26.000071

Abstract

References

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, demanding reliable and early prediction systems. This study proposes an optimized machine learning framework that integrates advanced data pre processing, dimensionality reduction, class balancing, and hyper parameter tuning to enhance CVD prediction accuracy. A combined dataset of 920 patient records from multiple clinical sources was cleaned, standardized, and processed using Principal Component Analysis (PCA) to reduce redundancy and multi collinearity among correlated medical attributes. Class imbalance was addressed using SMOTE, significantly improving minority class sensitivity. Six machine learning classifiers including Random Forest, Gradient Boosting, SVC, Decision Tree, AdaBoost, and an Ensemble Classifier were trained and evaluated under four configurations: with or without PCA and with or without hyper parameter tuning via Grid SearchCV. Results demonstrate that the integration of PCA and tuning consistently improvedmodel performance, with the Ensemble Classifier and Random Forest achieving accuracies and F1-scores. Significant improvements in recall and ROC-AUC values indicate better discriminatory power, especially for disease positive cases. Overall, the proposed pipeline provides a robust, generalizable approach for CVD prediction and highlights the importance of dimensionality reduction, data balancing, and parameter optimization in developing clinically reliable diagnostic models.

[1] A. Tiwari, A. Chugh, and A. Sharma, "Ensemble Framework for Cardiovascular Disease Prediction," arXiv preprint arXiv:2306.09989, 2023. [Online]. Available: https://arxiv.org/abs/2306.09989. [Accessed: Jan. 3, 2025].

[2] M. T. García-Ordás, M. Bayón-Gutiérrez, C. Benavides, J. Aveleira-Mata, and J. A. Benítez-Andrades, "Heart disease risk prediction using deep learning techniques with feature augmentation," arXiv preprint arXiv:2402.05495, 2024. [Online]. Available: https://arxiv.org/abs/2402.05495. [Accessed: Jan. 3, 2025].

[3] W.-H. Weng, S. Baur, M. Daswani, C. Chen, L. Harrell, S. Kakarmath, M. Jabara, B. Behsaz, C. Y. McLean, Y. Matias, G. S. Corrado, S. Shetty, S. Prabhakara, Y. Liu, G. Danaei, and D. Ardila, "Predicting cardiovascular disease risk using photoplethysmography and deep learning," arXiv preprint arXiv:2305.05648, 2023. [Online]. Available: https://arxiv.org/abs/2305.05648. [Accessed: Jan. 3, 2025].

[4] M. Shishehbori and M. Awan, "Incorporating psychological data into machine learning models for cardiovascular disease prediction," arXiv preprint arXiv:2401.17328, 2024. [Online]. Available: https://arxiv.org/abs/2401.17328. [Accessed: Jan. 3, 2025].

[5] M. Hossain, M. Islam, A. Rahman, and S. Hoque, "A Comparative Study of Tree-Based Machine Learning Methods for Cardiovascular Disease Detection," Scientific Reports, vol. 14, no. 1, p. 72819, 2024. [Online]. Available: https://www.nature.com/articles/s41598-024-72819-9. [Accessed: Jan. 3, 2025].

[6] M. T. García-Ordás, M. Bayón-Gutiérrez, C. Benavides, J. Aveleira-Mata, and J. A. Benítez-Andrades, "Combining machine learning and deep learning techniques for cardiovascular disease prediction," Eur. J. Med. Res., vol. 29, no. 1, pp. 20-30, 2024. [Online]. Available: https://eurjmedres.biomedcentral.com/articles/10.1186/s40001-024-02044-7. [Accessed: Jan. 3, 2025].

[7] Organization, W.H. (2023) Cardiovascular diseases, World Health Organization. Available at: https://www.who.int/healthtopics/cardiovascular-diseases (Accessed: 23 May 2023).

[8] MadhukarChavan, Shashank Kumar Singh, SaurabhBansod and Prashant Pal, “Design and Implementation of Heart Disease Prediction Using Artificial Neural Network” 2023 Eighth International Conference on Science Technology Engineering Mathematics (ICONSTEM) | 979-8-3503-4779-1/23/$31.00 ©2023 IEEE| DOI: 10.1109/ICONSTEM56934.2023.10142267.

[9] Barbara Martins, Diana Ferreira, Cristiana Neto, Antonio Abelha,Jose Machado , “Data Mining for Cardiovascular Disease Prediction,” ©Springer, 5 January 2021 .

[10] S. Patel, J. Patel and S. Tejalupadhyay,"Cardiovascular Disease prediction using Machine learning and Data Mining Technique", Journal - IJCSC, vol. 7, 2022. Available: 10.090592/IJCSC.2016.018.

[11] Malav, A., Kadam, K. and Kamat, P. prediction of heart disease using K-means and artificial neural network Availableat:https://www.researchgate.net/publication/319486202_Prediction_Of_Heart_Disease_Using_KMeans_and _Artificial_Neural_Network_as_H Hybrid_Approach_to_Improve_Accuracy (Accessed: 22 May 2023).

[12] NajmulHasan and YukunBao, “Comparing different feature selection algorithms for cardiovascular disease prediction,” © Springer,October 2020.

[13] M. A. Alim, S. Habib, Y. Farooq and A. Rafay 2020 Robust Heart Disease Prediction: A Novel Approach based on Significant Feature and Ensemble learning Model 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp 1–5.

[14] Dr. Joy IongZong Chen, P. Hengjinda, “Early Prediction of Coronary Artery Disease (CAD) by Machine Learning Method-A Comparative Study” Journal of Artificial Intelligence and Capsule Networks (2021) Vol.03/ No.01 http://irojournals.com/aicn/DOI: https://doi.org/10.36548/jaicn.2021.1.002.

[15] DivyaAnnepu, Gowtham G, “Cardio vascular Disease Prediction Using Machine Learning Techniques” International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072. Analytics

[16] KaziMostaqhridoy, Saiful Islam Akash, FahmidaAfroseDipti, MahamudulHasan, JesanAhammedovi, Md Al-Imran, Dr. SarwarJahan "Heart Disease Algorithm using Machine Learning Algorithms" 2023 4th International Conference on Big Data and Practices 10.1109/IBDAP58581.2023.10271997 (IBDAP) | 979-8-3503-0019- 2/23/$31.00 ©2023 IEEE | DOI:

[17] S. Ismaeel, A. Miri and D. Chourishi 2015 Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis 2015 IEEE Canada International Humanitarian Technology Conference (IHTC2015), pp 1–3

[18] Abdullah Alqahtani, ShtwaiAlsubai, Mohammed Sha, Lucia Vilcekova, and TalhaJaved, “Cardiovascular Disease Detection using Ensemble Learning,” Hindawi, August 2022.

[19] Dangare, C. and Apte, S. (2012) A data mining approach for prediction of heart disease using Neural Networks, SSRN. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstractid=2175569 (Accessed: 22 May 2023).

[20] Welcome to the UC Irvine Machine Learning Repository (no date) UCI Machine Learning Repository. Available at: https://archive.ics.uci.edu/ (Accessed: 19 June 2023).

[21] https://www.kaggle.com/sulianova/cardiovascular-disease-dataset

[22] C. J. Harrison and C. J. S. Gibbons, “Machine learning in medicine: a practical introduction to natural language processing,” BMC Medical Research Methodology, vol. 21, no. 1, p. 158, 2021.

[23] R. Kumar, P. Kumar, R. Tripathi, G. P. Gupta, A. K. M. N. Islam, and M. Shorfuzzaman, “Permissioned blockchainanddeep- learning for secure and efficient data sharing in industrial health care systems," IEEE Transactions on Industrial Informatics, p. 1, 2022.

[24] Karl PatrikKresoja, Matthias Unterhuber, Rolf Wachter,HolgerThiele,PhilippLurz, “A cardiologists guide to machine learning in cardiovascular disease prognosis prediction” Basic Research in Cardiology (2023) 118:10 https://doi.org/10.1007/s00395-023-00982-7 ©Springer, 20 March 2023.

[25] Sibo Prasad Patro, NeelamadhabPadhy, DukuruChiranjevi, “Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning” Evolutionary Intelligence https://doi.org/10.1007/s12065 020-00484-8, © Springer-Verlag GmbH Germany, part of Springer Nature 2020.

[26] SaravananSrinivasan, SubathraGunasekaran, SandeepKumarMathivanan, BenjulaAnbu Malar M. B, PrabhuJayagopal&GemmachisTeshiteDalu, “An active learning machine technique-based prediction of cardiovascular heart disease from UCI repository database”Scientific Reports | (2023) 13:13588 | https://doi.org/10.1038/s41598-023 40717-1.

[27] MadhukarChavan, Shashank Kumar Singh, SaurabhBansod and Prashant Pal, “Design and Implementation of Heart Disease Prediction Using Artificial Neural Network” 2023 Eighth International Conference on Science Technology Engineering and Mathematics(ICONSTEM) | 979-8-3503-4779- 1/23/$31.00 ©2023 IEEE| DOI: 10.1109/ICONSTEM56934.2023.10142.

[28] GhulabNabiAhamad, Shafiullah ,Hira Fatima, Imdadullah ,S. M. Zakariya, Mohamed Abbas,Mohammed S. Alqahtani ,and Mohammed Usman “Influence of Optimal Hyperparameters on the Performance of Machine Learning Algorithms for Predicting Heart Disease” Processes 2023, 11, 734. https://doi.org/10.3390/pr11030734.

[29] GamalSaad Mohamed Khamis and Sultan MunadiAlanazi “ Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques” Khamis and Alanazi BMC Medical Informatics and Decision Making (2023) 23:101 https://doi.org/10.1186/s12911-023-02179-3.

[30] Karna Vishnu Vardhana Reddy, IrraivanElamvazuthi, AzrinaAbd Aziz, SivajothiParamasivam, Hui Na Chua and SatyamurthyPranavanand “An Efficient Prediction System for Coronary Heart Disease Risk Using Selected Principal Components and Hyperparameter Optimization” Appl. Sci. 2023, 13, 118. https://doi.org/10.3390/app13010118.

[31] Maria Trigka and Elias Dritsas “Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models” Sensors 2023, 23,1193. https://doi.org/10.3390/s23031193.

[32] Abdullah Alqahtani ,ShtwaiAlsubai , MohemmedSha , Lucia Vilcekova, and TalhaJaved , “Cardiovascular Disease Detection using Ensemble Learning, ” Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 5267498, 9 pages https://doi.org/10.1155/2022/5267498,August 2022.

Keywords: Hyper-parameter tuning, Classification Algorithm, Prediction, Heart Disease, and Dimension reduction.

Download PDF Pages ( 35-42 ) Download Full Article (PDF)

←Previous Next →

SPU - Journal of Science, Technology and Management Research

An Official Publication of Sankalchand Patel University, Visnagar, Gujarat, India.

Special Edition ICAICS-2026 | 1 (March)

A Comparative Performance Analysis of Machine Learning Models for Cardiovascular Disease Prediction Using Hyper Parameter Tuning