Automated Cardiovascular Disease Prediction Models: A Comparative Analysis




Cardiovascular disease, mortality, prediction, machine learning, heart-attack, attributes


INTRODUCTION: Cardiovascular disease (CVD) is one of the primary causes of the increased mortality rate universally. Therefore, automated methods for early prediction of CVD are of utmost importance to prevent the disease.

OBJECTIVES: In this study, we have pointed out the major advantages, drawbacks, and the scope of enhancing the prediction accuracy of the existing automated cardiovascular disease prediction methods. In addition to that, we have analyzed various combinations of attributes that can help in prediction at the earliest.

METHODS: We have exploited various machine learning models to analyse their performances in predicting the CVD at the earliest.

RESULTS: For a publicly available database, the Artificial Neural Network attained the highest accuracy of 88.5% and recall of 90%.

CONCLUSION: We justified the notion that it will be beneficial to identify potential physiological and behavioural attributes to predict CVD accurately as early as possible.


Download data is not yet available.


L. Yang et al., (2020) Study of cardiovascular disease prediction model based on random forest in eastern China, Sci. Rep., 10(1), pp. 1–8, 2020. DOI:

S. Kanjilal et al., (2008) Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians, Vasc. Health Risk Manag., 4(1), pp. 199–211. DOI:

Published by the World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization. .

Y. Ruan et al., (2018 ) Cardiovascular disease (CVD) and associated risk factors among older adults in six low-and middle-income countries: results from SAGE Wave 1, BMC Public Health, 18(1), pp. 778. DOI:

Cardiovascular diseases (CVDs). [Online]. Available: [Accessed: 20-Jan-2021].

“HO | The Atlas of Heart Disease and Stroke, WHO, 2010.

J. A. A. G. Damen et al., (2016 ) Prediction models for cardiovascular disease risk in the general population: Systematic review, BMJ (Online), 353(i2416). BMJ Publishing Group, 16-May. DOI:

W. H. Lin, H. Zhang, and Y. T. Zhang, (2013) Investigation on cardiovascular risk prediction using physiological parameters, Computational and Mathematical Methods in Medicine, vol. 2013. Taylor and Francis Ltd. DOI:

F. P. Cappuccio, P. Oakeshott, P. Strazzullo, and S. M. Kerry, (2002) Application of Framingham risk estimates to ethnic minorities in United Kingdom and implications for primary prevention of heart disease in general practice: Cross sectional population based study, Br. Med. J., 325(7375), pp. 1271–1274. DOI:

A. Kumar, R. Gyawali, and S. Agarwal, (2020) Cardiovascular disease prediction using machine learning tools, in Advances in Intelligent Systems and Computing, 1085, pp. 441–451. DOI:

C. S. Prakash, M. Madhu Bala, and A. Rudra, (2020) Data Science Framework - Heart Disease Predictions, Variant Models and Visualizations, in 2020 International Conference on Computer Science, Engineering and Applications, ICCSEA 2020, pp. 1–4. DOI:

S. Xu, Z. Zhang, D. Wang, J. Hu, X. Duan, and T. Zhu, (2017) Cardiovascular risk prediction method based on CFS subset evaluation and random forest classification framework, in 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017, pp. 228–232. DOI:

M. Nakai et al., (2020) Development of a cardiovascular disease risk prediction model using the suita study, a population-based prospective cohort study in Japan, J. Atheroscler. Thromb., 27(11), pp. 1160–1175. DOI:

M. Rezaee, I. Putrenko, A. Takeh, A. Ganna, and E. Ingelsson, (2020) Development and validation of risk prediction models for multiple cardiovascular diseases and Type 2 diabetes, PLoS One, 15(7), p. e0235758, Jul. DOI:

S. R. Alty, S. C. Millasseau, P. J. Chowienczyk, and A. Jakobsson, (2006) Cardiovascular disease prediction using support vector machines, pp. 376–379.

T. D. Pham et al., (2008) Computational prediction models for early detection of risk of cardiovascular events using mass spectrometry data, IEEE Trans. Inf. Technol. Biomed., 12(5), pp. 636–643. DOI:

C. Y. Zhu, S. Q. Chi, R. Z. Li, D. Y. Tong, Y. Tian, and J. S. Li, (2017) Design and development of a readmission risk assessment system for patients with cardiovascular disease, in Proceedings - 2016 8th International Conference on Information Technology in Medicine and Education, ITME 2016, pp. 121–124. DOI:

H. D. Park, Y. Han, and J. H. Choi, (2018) Frequency-Aware Attention based LSTM Networks for Cardiovascular Disease, in 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018, pp. 1503–1505. DOI:

C. C. Peng, Y. C. Lai, C. W. Huang, J. G. Wang, S. H. Wang, and Y. Z. Wang, (2020) Cardiovascular Diseases Prediction Using Artificial Neural Networks: A Survey, in 2nd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2020, ECBIOS 2020, pp. 141–144. DOI:

R. Ghongade and A. A. Ghato, (2007) A brief performance evaluation of ECG feature extraction techniques for artificial neural network based classification, in IEEE Region 10 Annual International Conference, Proceedings/TENCON, pp. 1-4.

M. H. F. M. Jalil, M. F. Saaid, A. Ahmad, and M. S. A. M. Ali, (2014) Arrhythmia modelling via ECG characteristic frequencies and artificial neural network, in Proceedings - 2014 IEEE Conference on System, Process and Control, ICSPC 2014, pp. 121-126.

H. Haseena, P. K. Joseph, and A. T. Mathew, (2009) Artificial neural network based ECG arrhythmia classification, J. Mech. Med. Biol., 9(4), pp. 507-525. DOI:

T. Debnath, M. Hasan, and T. Biswas, (2017) Analysis of ECG signal and classification of heart abnormalities using artificial neural network, in Proceedings of 9th International Conference on Electrical and Computer Engineering, ICECE 2016, pp. 353-356. DOI:

A. A. S. Raj, N. Dheetsith, S. S. Nair, and D. Ghosh, (2015) Auto analysis of ECG signals using artificial neural network,” in 2014 International Conference on Science Engineering and Management Research, ICSEMR 2014, pp. 1-4.

H. Gothwal, S. Kedawat, and R. Kumar, (2011) Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network, J. Biomed. Sci. Eng., 4, pp. 289-296. DOI:

R. Ceylan and Y. Özbay, (2007) Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network, Expert Syst. Appl., 33 (2), pp. 286-295. DOI:

N. K. Dewangan and S. P. Shukla, (2017) ECG Arrhythmia classification using discrete wavelet transform and artificial neural network, in 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings, pp. 1892-1896. DOI:

N. G. B. Amma, (2012) Cardiovascular disease prediction system using genetic algorithm and neural network, in 2012 International Conference on Computing, Communication and Applications, ICCCA 2012, pp. 1-5. DOI:

Wiharto, H. Kusnanto, and H. Herianto, (2017) Hybrid system of tiered multivariate analysis and artificial neural network for coronary heart disease diagnosis, Int. J. Electr. Comput. Eng., 7(2), pp. 1023-1031. DOI:

D. Gao, M. Madden, D. Chambers, and G. Lyons, (2005) Bayesian ANN classifier for ECG arrhythmia diagnostic system: A comparison study, in Proceedings of the International Joint Conference on Neural Networks, 4, pp. 2383-2388.

S. Nita, S. Bitam, and A. Mellouk, (2018) An Enhanced Random Forest for Cardiac Diseases Identification based on ECG signal, in 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018, pp. 1339-1344. DOI:

A. Darmawahyuni, S. Nurmaini, and F. Firdaus, (2019) Coronary Heart Disease Interpretation Based on Deep Neural Network, Comput. Eng. Appl. J., 8(2), pp. 1-12. DOI:

R. M. Conroy et al., (2003) Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project,” Eur. Heart J., 24, pp. 987–1003. DOI:

J. Hippisley-Cox et al., (2008) Predicting cardiovascular risk in England and Wales: Prospective derivation and validation of QRISK2, BMJ, 336, pp. 1475–1482. DOI:

P. M. Ridker, N. P. Paynter, N. Rifai, J. M. Gaziano, and N. R. Cook, (2008) C-reactive protein and parental history improve global cardiovascular risk prediction: The Reynolds risk score for men, Circulation, 118, pp. 2243–2251. DOI:

P. M. Ridker, J. E. Buring, N. Rifai, and N. R. Cook, (2007) Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: The Reynolds Risk Score, J. Am. Med. Assoc., 297, pp. 611–619. DOI:

G. Assmann, P. Cullen, and H. Schulte, (2002 ) Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the Prospective Cardiovascular Münster (PROCAM) study, Circulation, 105, pp. 310–315. DOI:

C. McGorrian et al., (2011) Estimating modifiable coronary heart disease risk in multiple regions of the world: The INTERHEART Modifiable Risk Score, Eur. Heart J., 32, pp. 581–589. DOI:

A. Gholamy, V. Kreinovich, and O. Kosheleva, (2018) Technical Report on Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation (El Paso: Computer Science Department, The University of Texas at El Paso) 1209.

R. Hajar, (2017) Risk Factors for Coronary Artery Disease: Historical Perspectives, Heart Views, 18(3), p. 109. DOI:

D. M. T. Tran, N. Lekhak, K. Gutierrez, and S. Moonie, (2021) Risk factors associated with cardiovascular disease among adult Nevadans, PLoS One, 16(2), p. e0247105, Feb. DOI:




How to Cite

Choudhury T, Choudhury B. Automated Cardiovascular Disease Prediction Models: A Comparative Analysis. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 May 29 [cited 2023 Oct. 4];9:e6. Available from: