Heart Disease Risk Prediction: A Comparison of Machine Learning Techniques


  • Ayşe Banu Birlik Department of Medical Services and Techniques, Beykoz University, Istanbul, Turkey Graduate School of Engineering and Natural Sciences, Health System Engineering Program, Istanbul Medipol University, Istanbul, Turkey


Classification Algorithm, Decision Making, Machine Learning, Risk Prediction


Healthcare services have once again demonstrated their worldwide importance under the pandemic conditions. Under the leadership of Industry (4.0), data mining continues to develop in the field of health. Data mining prediction tool act a critical role in healthcare. Heart disease is the most dangerous noncommunicable disease in the world. To predict heart disease, a variety of data mining techniques are used. The study's goal is to use classification algorithms to predict the occurrence of heart disease in an individual. In the study, a dataset consisting of 14 variables belonging to 303 patients accessed from the Kaggle site was used. 75% of the dataset is split into training sets and 25% into test sets in order to train and test the model. Classification performances were compared using K-Nearest Neighborhood (KNN), Random Forest (RF) and Decision Tree (DT) algorithms. As a result of the study, it was determined that the classification accuracy of KNN, RF and DT algorithms was 88.16%, 89.47% and 84.21%, respectively.


. Gregory A. Roth, e. a. (2020). Global Burden of Cardiovascular Diseases and Risk Factors 1990–2019: Update From the GBD 2019 Study. Journal of the American College of Cardiology, 76(25), 2982-3021.

. G.A. Mensah, G. R. (2019). The global burden of cardiovascular diseases and risk factors: 2020 and beyond . J Am Coll Cardiol, 2529-2532.

. T. Vos, S. L. (2020). Global burden of 369 diseases and injuries in 204 countries and territories 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 Lancet. 1204-1222.

. Gregory A. Roth, G. A. (2020). The Global Burden of Cardiovascular Diseases and Risks: A Compass for Global Action. Journal of the American College of Cardiology, 76(25), 2980-2981.

. Judith Mackay, G. M. (2004). The atlas of heart disease and stroke . World Health Organization.

. Tekin, A. (2018). The calculation of cardiovascular mortal risk and the evaluation of the level of knowledge of cardiovascular risk factors with score equality between 40-65 years age. ?zmir: ?zmir Katip Çelebi Üniversitesi T?p Fakültesi Aile Hekimli?i Anabilim Dal?.

. C. Beulah Christalin Latha, S. C. (2019). Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, 16, 100203.

. Lamido Yahaya, N. D. (2020). A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques. American Journal of Artificial Intelligence., 4(1), 20-29.

. Azmi, J., Arif, M., Nafis, M. T., Alam, M. A., Tanweer, S., & Wang, G. (2022). A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data. Medical Engineering and Physics, 105(February), 103825. https://doi.org/10.1016/j.medengphy.2022.103825

. Muhammad, Y., Tahir, M., Hayat, M., & Chong, K. T. (2020). Early and accurate detection and diagnosis of heart disease using intelligent computational model. Scientific Reports, 10(1), 1–17. https://doi.org/10.1038/s41598-020-76635-9

. Nestor, P. (2020). Using Machine Learning Classification Methods to Detect the Presence of Heart Disease. Technological University Dublin

. Kartik Budholiya, S. K. (2020). An optimized XGBoost based diagnostic system for effective prediction of heart disease. Journal of King Saud University Computer and Information Sciences.

. M. Anbarasi, E. A. (2010). Enhanced Prediction of Heart Disease with Feature Subset Selection Using Genetic Algorithm. International Journal of Engineering Science and Technology, 5370-5376.

. Kalluri, H. K. (2020). A Deep Learning Method for Prediction of Cardiovascular Disease Using Convolutional Neural Network. Revue d intelligence artificielle, 601-606.

. K. G. Dinesh, K. A. (2018). Prediction of Cardiovascular Disease Using Machine Learning Algorithms. 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), (s. 1-7).

. D. Krishnani, A. K. (2019). Prediction of Coronary Heart Disease using Supervised Machine Learning Algorithms . TENCON 2019 - 2019 IEEE Region 10 Conference , (s. 367-372).

. Padmaja, B., Srinidhi, C., Sindhu, K., Vanaja, K., Deepika, N. M., & Krishna Rao Patro, E. (2021). Early and Accurate Prediction of Heart Disease Using Machine Learning Model. Turkish Journal of Computer and Mathematics Education, 12(6), 4516–4528.

. Dhankhar, A., & Jain, S. (2021). Prediction of diabetes disease using machine learning algorithms. In N. Gupta, P. Chatterjee, & T. Choudhury (Eds.), In Smart and Sustainable Intelligent Systems (pp. 115–126). Scrivener Publishing LLC. https://doi.org/10.1002/9781119752134.ch8

. Guruprasad, S., Mathias, V. L., & Dcunha, W. (2021). Heart Disease Prediction Using Machine Learning Techniques. 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques, ICEECCOT 2021 - Proceedings, 1(6), 762–766. https://doi.org/10.1109/ICEECCOT52851.2021.9707966

. Gao, X. Y., Amin Ali, A., Shaban Hassan, H., & Anwar, E. M. (2021). Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method. Complexity, 2021. https://doi.org/10.1155/2021/6663455

. 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

. Géron, A. (2017). Hands On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly Media.

. J., C. C. (2015). Data Classification Algorithms and Applications. New York, USA: Taylor & Francis Group.

. Gökta?, M. Y. (2020). Veri Bilimi Uygulamalar?n?n Hastal?k Te?hisinde Kullan?lmas?: Kalp Krizi Örne?i. Journal of Information Systems and Management Research, 26-32.

. Matloff, N. (2017). Statistical Regression and Classification From Linear Models to Machine Learning. USA: Taylor & Francis Group.




How to Cite

Ayşe Banu Birlik. (2023). Heart Disease Risk Prediction: A Comparison of Machine Learning Techniques. International Journal of Sciences: Basic and Applied Research (IJSBAR), 67(1), 217–226. Retrieved from https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14965