Performance evaluation of machine learning techniques for heart disease prediction: An overview
Resumo
One of the most common and serious diseases is heart disease, as it is one of the major causes of death globally. Heart disorders come in many forms, including arrhythmia, congenital heart disease, and atherosclerosis. Patients with heart disease experience a variety of symptoms, such as dizziness, chest discomfort, and excessive perspiration. Heart disease is primarily caused by smoking, high blood pressure, diabetes, obesity, and other risk factors. Developing an affordable and non-invasive approach to predicting heart disease is necessary. Creating a system that accurately predicts heart disease with minimal errors is essential. Consequently, machine learning is vital for predicting the risk of future cardiopathy by analysing the patient's health conditions and past medical history to decrease the possibility of mortality from heart disease. Machine learning (ML) has rapidly advanced in recent years, and its application in medical sciences can revolutionize how complex diagnostic and prognostic evaluations are conducted at the individual patient level. ML helps predict the risk of developing heart disease based on the patient's current and historical medical conditions to decrease the chances of death due to heart disease. ML techniques, including Random Forest, ANN, Linear Regression (LR), Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Gradient Boosting, and Decision Tree (DT), are utilized to create the machine learning model. This paper presents an overview of the Heart Disease Prediction system using Machine Learning techniques. A detailed tabular comparison of the reviewed papers is also included.
Downloads
Referências
Ali, L., Niamat, A., Khan, J. A., Golilarz, N. A., Xingzhong, X., Noor, A., Nour, R., & Bukhari, S. A. C. (2019). An optimized stacked support vector machine-based expert system for the effective prediction of heart failure. IEEE Access, 7, 54007-54014. https://doi.org/10.1109/ACCESS.2019.2909969
Ambrish, G., Ganesh, B., Ganesh, A., Srinivas, C., Dhanraj, & Mensink, K. (2022). Logistic regression technique for the prediction of cardiovascular disease. Global Transitions Proceedings, 3(1), 127-130. https://doi.org/10.1016/j.gltp.2022.04.008
Ansari, M. F., Kaur, B. A., & Kaur, H. (2021). A prediction of heart disease using machine learning algorithms. In J. IZ. Chen, J. M. R. S. Tavares, S. Shakya, & A. M. Iliyasu (Eds), Image processing and capsule networks (pp.497-504). Springer. https://doi.org/10.1007/978-3-030-51859-2_45
Asadi, S., Roshan, S., & Kattan, M. W. (2021). Random forest swarm optimization-based for heart disease diagnosis. Journal of Biomedical Informatics, 115, 1-13. https://doi.org/10.1016/j.jbi.2021.103690
Austin, P. C. Tu, J. V., Ho, J. E., Levy, D., & Lee, D. S. (2013). Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology, 66, 398-407. https://doi.org/10.1016/j.jclinepi.2012.11.008
Chauhan, R., Jangade, R., & Rekapally, R. (2018). Classification model for prediction of heart disease. In M. Pant, K. Ray, T. Sharma, S. Rawat, & A. Bandyopadhyay (Eds.), Soft computing: Theories and applications. Advances in intelligent systems and computing (v. 584) (pp.707-714). Springer. https://doi.org/10.1007/978-981-10-5699-4_67
Chitra, R., & Seenivasagam, V. (2014). Risk prediction of heart disease based on swarm optimized neural network. In S. Patnaik, & X. Li (Eds), Proceedings of International Conference on Computer Science and Information Technology (v. 255) (pp. 707-714). Springer. https://doi.org/10.1007/978-81-322-1759-6_81
Diwakar, M., Tripathi, A., Joshi, K., Memoria, M., Singh, P., & Kumar, N. (2020). Latest trends on heart disease prediction using machine learning and image fusion. Materialstoday Proceedings, 37(2), 3213-3218. https://doi.org/10.1016/j.matpr.2020.09.078
Dwivedi, A. K. (2016). Performance evaluation of different machine learning techniques for the prediction of heart disease. Neural Computing & Applications, 29, 685-693. https://doi.org/10.1007/s00521-016-2604-1
Gavhane, A., Kokkula, G., Pandya, I., & Devadkar, K. (2018). Prediction of heart disease using machine learning. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1275-1278). IEEE. https://doi.org/10.1109/ICECA.2018.8474922
Kavitha, M., Gnaneswar, G., Dinesh, R., Rohith Sai, Y., & Sai Suraj, R. (2021). Heart disease prediction using hybrid machine learning model. In International Conference on Inventive Computation Technologies (pp. 1329-1333). IEEE. https://doi.org/10.1109/ICICT50816.2021.9358597
Keya, M. S., Shamsojjaman, M. Hossain, F., Akter, F., Islam, F., & Emon, M. U. (2021). Measuring the heart attack possibility using different types of machine learning algorithms. In 2021 International Conference on Artificial Intelligence and Smart Systems (pp. 74-78). IEEE. https://doi.org/10.1109/ICAIS50930.2021.9395846
Khan, Z., Mishra, D. K., Sharma, V., & Sharma, A. (2020). Empirical study of various classification techniques for heart disease prediction. In International conference on computing, communication and automation (pp. 57-62). IEEE. https://doi.org/10.1109/ICCCA49541.2020.9250852
Latha, C. B. C., & Jeeva, S. C. (2019). Improving the accuracy of the prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, 16, 1-9, https://doi.org/10.1016/j.imu.2019.100203
Long, N. C., Meesad, P., & Unger, H. (2015). A highly accurate firefly-based algorithm for heart disease prediction. Expert Systems with Applications, 42(21), 8221-8231. https://doi.org/10.1016/j.eswa.2015.06.024
Louridi, N., Douzi, S., & El Ouahidi, B. (2021). Machine learning‑based identification of patients with a cardiovascular defect. Journal of Big Data, 8(133), 1-15. https://doi.org/10.1186/s40537-021-00524-9
Maini, E., Venkateswarlu, B., Maini, B., & Marwaha, D. (2020). Machine learning based heart disease prediction system for Indian population: An exploratory study done in South India. Medical Journal Armed Forces India, 77(3), 302-311. https://doi.org/10.1016/j.mjafi.2020.10.013
Maji, S., & Arora, S. (2019). Decision tree algorithms for prediction of heart disease. In S. Fong, S. Akashe, & P. Mahalle (Eds.), Information and communication technology for competitive strategies. Lecture notes in networks and systems (v. 40) (pp. 447-454). Springer. https://doi.org/10.1007/978-981-13-0586-3_45
Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554. https://doi.org/10.1109/ACCESS.2019.2923707
Pathan, M. S., Nag, A., Pathan, M. M. & Dev, S. (2022). Analyzing the impact of feature selection on the accuracy of heart disease prediction. Healthcare Analytics, 2, 1-9. https://doi.org/10.1016/j.health.2022.100060
Rani, P., Kumar, R., Ahmed, N. M. O. S., & Jain, A. (2021). A decision support system for heart disease prediction based upon machine learning. Journal of Reliable Intelligent Environments, 7, 263–275. https://doi.org/10.1007/s40860-021-00133-6
Shah, D., Patel, S., & Bharti, S. K. (2020a). Heart disease prediction using machine learning techniques. SN Computer Science, 1(345). https://doi.org/10.1007/s42979-020-00365-y
Shah, S. M. S., Shah, F. A., Hussain, S. A., & Batool, S. (2020b). Support vector machines-based heart disease diagnosis using feature subset, wrapping selection and extraction methods. Computers and Electrical Engineering, 84, 106628. https://doi.org/10.1016/j.compeleceng.2020.106628
Shilaskar, S., & Ghatol, A. (2013). Feature selection for medical diagnosis: Evaluation for cardiovascular diseases. Expert Systems with Applications, 40(10), 4146-4153. https://doi.org/10.1016/j.eswa.2013.01.032
Singh, Y. K., Sinha, N., & Singh, S. K. (2017). Heart disease prediction system using random forest. In M. Singh, P. Gupta, V. Tyagi, A. Sharma, T. Ören, & W. Grosky (Eds.), Advances in computing and data sciences. Communications in computer and information science (v. 721) (pp.613-623). Springer. https://doi.org/10.1007/978-981-10-5427-3_63
Swain, D., Pani, S. K., & Swain, D. (2018). A metaphoric investigation on prediction of heart disease using machine learning. In International Conference on Advanced Computation and Telecommunication (pp. 1-6). IEEE. https://doi.org/10.1109/ICACAT.2018.8933603
World Health Organization [WHO]. (2020). Cardiovascular diseases. https://www.who.int/healthtopics/cardiovascular-diseases.
Yang, H., & Garibaldi, J. M. (2015). A hybrid model for automatic identification of risk factors for heart disease. Journal of Biomedical Informatics, 58(Suppl.), 171-182. https://doi.org/10.1016/j.jbi.2015.09.006
Copyright (c) 2025 Dhanashri Shankar Karande, Shailendrakumar Mahadeo Mukane (Autor)

This work is licensed under a Creative Commons Attribution 4.0 International License.
DECLARAÇÃO DE ORIGINALIDADE E DIREITOS AUTORAIS
Declaro que o presente artigo é original, não tendo sido submetido à publicação em qualquer outro periódico nacional ou internacional, quer seja em parte ou em sua totalidade.
Os direitos autorais pertencem exclusivamente aos autores. Os direitos de licenciamento utilizados pelo periódico é a licença Creative Commons Attribution 4.0 (CC BY 4.0): são permitidos o compartilhamento (cópia e distribuição do material em qualqer meio ou formato) e adaptação (remix, transformação e criação de material a partir do conteúdo assim licenciado para quaisquer fins, inclusive comerciais.
Recomenda-se a leitura desse link para maiores informações sobre o tema: fornecimento de créditos e referências de forma correta, entre outros detalhes cruciais para uso adequado do material licenciado.