A Comparison Analysis of Machine Learning Algorithms on Cardiovascular Disease Prediction

Main Article Content

Lakkala Jayasree
D. Usha

Abstract

People nowadays are engrossed in their daily routines, concentrating on their jobs and other responsibilities while ignoring their health. Because of their hurried lifestyles and disregard for their health, the number of people becoming ill grows daily. Furthermore, most of the population suffers from a disease such as cardiovascular disease. Cardiovascular disease kills 35% of the world's population, according to W.H.O. A person's life can be saved if a heart disease diagnosis is made early enough. Still, it can also be lost if the diagnosis is constructed incorrectly. Therefore, predicting heart disease will become increasingly relevant in the medical sector. The volume of data collected by the medical industry or hospitals, on the other hand, can be overwhelming at times. Time-series forecasting and processing using machine learning algorithms can help healthcare practitioners become more efficient. In this study, we discussed heart disease and its risk factors and machine learning techniques and compared various heart disease prediction algorithms. Predicting and assessing heart problems is the goal of this research.

Article Details

How to Cite
Jayasree, L. ., & Usha, D. . (2022). A Comparison Analysis of Machine Learning Algorithms on Cardiovascular Disease Prediction. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(3), 14–22. Retrieved from http://ijfrcsce.org/index.php/ijfrcsce/article/view/2087
Section
Articles

References

Sengan, Sudhakar, et al. "Secured and privacy-based I.D.S. for healthcare systems on E-medical data using machine learning approach." International Journal of Reliable and Quality E-Healthcare (IJRQEH) 11.3 (2022): 1-11.

Fukuoka, Yoshimi, and Yoo Jung Oh. "Perceived Heart Attack Likelihood in Adults with a High Diabetes Risk." Heart & Lung 52 (2022): 42-47.

M. Tarawneh and O. Embarak, "Hybrid approach for heart disease prediction using data mining techniques," Advances in Internet, Data and Web Technologies, in Proceedings of the International Conference on Emerg- ing Internetworking, Data & Web Technologies, pp. 447–454, Springer, Fujairah Campus, U.A.E., February 2019

F. Jabeen, M. Maqsood, M. A. Ghazanfar et al., "An iot based efficient hybrid recommender system for cardiovascular disease," Peer-to-Peer Networking and Applications, vol. 12, no. 5, pp. 1263–1276, 2019.

Ali, Md Mamun, et al. "Heart disease prediction using supervised machine learning algorithms: performance analysis and comparison." Computers in Biology and Medicine 136 (2021): 104672.

Javan, Samaneh Layeghian, and Mohammad Mehdi Sepehri. "A predictive framework in healthcare: Case study on cardiac arrest prediction." Artificial Intelligence in Medicine 117 (2021): 102099.

P. Melillo, N. De Luca, M. Bracale, and L. Pecchia, "Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability," IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 3, pp. 727–733, 2013.

M. M. A. Rahhal, Y. Bazi, H. Alhichri, N. Alajlan, F. Melgani, and R. R. Yager, "Deep learning approach for active classification of electrocardiogram signals," Information Sciences, vol. 345, pp. 340–354, 2016

R. Zhang, S. Ma, L. Shanahan, J. Munroe, S. Horn, and S. Speedie, "Automatic methods to extract New York heart association classification from clinical notes," in Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1296–1299, IEEE, Kansas City, MO, U.S.A., November 2017

M.-S. Yang and Y. Nataliani, "A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy," IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 817– 835, 2018.

R. S. Singh, B. S. Saini, and R. K. Sunkaria, "Detection of coronary artery disease by reduced features and extreme learning machine," Medicine and Pharmacy Reports, vol. 91, no. 2, pp. 166–175, 2018

R. Rajagopal and V. Ranganathan, "Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification," Biomedical Signal Processing and Control, vol. 34, pp. 1–8, 2017

D. Zhang, L. Zou, X. Zhou, and F. He, "Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer," IEEE Access, vol. 6, pp. 28936–28944, 2018.

Harvard Medical School, "*roughout life, heart attacks are twice as common in men as women," 2020, https://www. health.harvard.edu/heart-health/throughout-life-heartattacks-are-twice-as-common-in-men-than-women

A. K. Garate-Escamila, A. Hajjam El Hassani, and E. Andr' es, ` "Classification models for heart disease prediction using feature selection and P.C.A.," Informatics in Medicine Unlocked, vol. 19, Article ID 100330, 2020.

Mohan, Senthilkumar, Chandrasegar Thirumalai, and Gautam Srivastava, "Effective heart disease prediction using hybrid machine learning techniques." IEEE Access 7 (2019): 81542-81554.

Ali, Liaqat, et al, "An optimized stacked support vector machines based expert system for the effective prediction of heart failure." IEEE Access 7 (2019): 54007-54014.

D. Zhang, L. Zou, X. Zhou, and F. He, "Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer," IEEE Access, vol. 6, pp. 28936–28944, 2018

R. S. Singh, B. S. Saini, and R. K. Sunkaria, "Detection of coronary artery disease by reduced features and extreme learning machine," Medicine and Pharmacy Reports, vol. 91, no. 2, pp. 166–175, 2018.

R. Chen, N. Sun, X. Chen, M. Yang, and Q. Wu, "Supervised feature selection with a stratified feature weighting method," IEEE Access, vol. 6, pp. 15087–15098, 2018.

M.-S. Yang and Y. Nataliani, "A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy," IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 817– 835, 2018

S. Kumar, "Predicting and diagnosing of heart disease using machine learning algorithms," International Journal of Engineering and Computer Science, vol. 6, no. 6, pp. 2319–7242, 2017.

R. Rajagopal and V. Ranganathan, "Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification," Biomedical Signal Processing and Control, vol. 34, pp. 1–8, 2017.

R. Zhang, S. Ma, L. Shanahan, J. Munroe, S. Horn, and S. Speedie, "Automatic methods to extract New York heart association classification from clinical notes," in Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1296–1299, IEEE, Kansas City, MO, U.S.A., November 2017

S. S. Khan and S. M. K. Quadri, "Prediction of angiographic disease status using rule based data mining techniques," Biological Forum—An International Journal, vol. 8, no. 2, pp. 103–107, 2016.

B. Dun, E. Wang, and S. Majumder, "Heart disease diagnosis on medical data using ensemble learning," 2016.

Bharti, Rohit, et al. "Prediction of heart disease using a combination of machine learning and deep learning." Computational intelligence and neuroscience 2021 (2021).

Obasi, Thankgod, and M. Omair Shafiq. "Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases." 2019 IEEE international conference on big data (big data). IEEE, 2019.

Kavitha, M., et al. "Heart disease prediction using hybrid machine learning model." 2021 6th International Conference on Inventive Computation Technologies (ICICT). IEEE, 2021.

Jindal, Harshit, et al. "Heart disease prediction using machine learning algorithms." I.O.P. Conference Series: Materials Science and Engineering. Vol. 1022. No. 1. I.O.P. Publishing, 2021.

Kondababu, A., et al. "A comparative study on machine learning based heart disease prediction." Materials Today: Proceedings (2021).

S. Manikandan, "Heart attack prediction system," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017, pp. 817- 820

Hlaudi Daniel Masethe, Mosima Anna Masethe, "Prediction of Heart Disease using classification Algorithm", Proceedings of the World Congress on Engineering & Computer Science 2014, WCECS.

M. A. Jabbar and S. Samreen, "Heart disease prediction system based on hidden naïve bayes classifier," 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), Bangalore, 2016, pp. 1-5.

Patel, S. B., Yadav, P. K. and Shukla, D. D., "Predict the diagnosis of heart disease patients using classification mining techniques", IOSR Journal of Agriculture and Veterinary Science, Vol. 4, No. 2, (2013), 61-64.

Obasi, Thankgod, and M. Omair Shafiq. "Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases." 2019 IEEE international conference on big data (big data). IEEE, 2019.

Kumar, Mukesh & Shambhu, Shankar & Sharma, Abha. (2018). Classification of Heart Diseases Patients using Data Mining Techniques. 10.13140/RG.2.2.11358.28486/1

Khateeb, Nida, Usman Muhammad "Efficient Heart Disease Prediction System using K-Nearest Neighbor Classification Technique" Proceedings of the International Conference on Big Data and Internet of Things, London, United Kingdom, December 20-22, 2017