Journal Press India®

Forecasting the Risk of Coronary Heart Diseases using Machine Learning Algorithms

Vol 3 , Issue 2 , July - December 2023 | Pages: 133-152 | Research Paper

Author Details ( * ) denotes Corresponding author

1. * Lakshmi J V N, Associate Professor, Computer Science, Patel Institute of Management and sciences, Bangalore, Karnataka, India (
2. Anirban Das, Software, Engineer, LJMU, Bangalore, Karnataka, India (

There are many emerging technologies such as machine learning and data analytics that offer promising solutions to healthcare challenges, biomedical communities, and patient care. Early detection of disease symptoms helps improve disease management strategies. Early detection also helps with disease symptom control and efficient therapy. In this study, we present a complete preprocessing strategy to predict coronary heart disease (CHD). The method includes computing null values, standardizing, categorizing, normalizing, resampling, and finally predicting. The purpose of the study is to predict CHD using machine learning techniques such as Random Forest, k-nearest neighbors, decision trees, logistic regression, and gradient boosting. We propose K-fold cross validation to provide predictability across the data. We test these algorithms on 4240 records from the “Framingham Heart Study” dataset. We also use a feature selection algorithm to reduce the dimensionality problem while keeping the computational complexity close to acceptable accuracy. The feature selection algorithm reduces the dimensionality problem and keeps the computational complexity close to acceptable accuracy. To predict accurately the risk of heart disease and to assess if a person has a risk of CHD, a new ensemble method using gradient boosting, random forest and k-nearest neighbor with majority vote has been tested with 96.16% accuracy and 0.96 ROC-AUC score. The experiments show that advances in machine learning, combined with predictive analytics, offer a potential environment for finding intelligent solutions, showing the potential of prediction in the field of cardiovascular disease and beyond.


Coronary Heart Disease; Gradient Boosting; Random Forest; KNN; Early detection; Machine Learning

  1. Alanazi, R. (2022). Identification and prediction of chronic diseases using Machine Learning approach. Journal of Healthcare Engineering, 6, 1-9. Retrieved from DOI:10.1155/2022/2826127
  2. Alex, M. & Shaji, S. (2019). Prediction and diagnosis of heart disease patients using data mining technique. International Conference on Communication and Signal Processing. Retrieved from DOI:10.1109/ICCSP.2019.8697977
  3. Anbuselvan, P. (2020). Heart disease prediction using machine learning techniques. International Journal of Engineering Research and Technology, 9(11), 515-518.
  4. Asif, S., Wenhui, Y., Jinhai, S. & Jin, H. (2021). An ensemble machine learning method for the predicton of heart disease. 4th International Conference on Artficial Intelligence and Big Data. Retrieved from 10.1109/ICAIBD51990.2021.9459010
  5. Bharti, R., Khamparia, A., Shabaz, M. & Dhiman, G. (2021). Predcition of heart disease using a combination of machine learning and deep learning. Computational Intelligence and Neuroscience, 1687-5273. Retrieved from DOI:10.1155/2021/8387680
  6. Cardiovascular diseases (2022 Feb). Retrieved from healthtopics/cardiovasculardiseases.
  7. Chen, C. & Zhang, X. (2021). Early warning methods of epidemiological risks based on data mining. International Conference on High Performance Big Data & Intelligent Systems. Retrieved from DOI:10.1109/HPBDIS53214.2021.9658444
  8. Kanchan, B. D. & Mahale, K. M. (2017). Study of machine learning algorithms for special disease prediction using principal of component analysis. International Conference on Global Trends in Signal Processing, Information computing and communication. Retrieved from 10.1109/ICGTSPICC.2016.7955260
  9. Dinesh, K. G., Arumugaraj, K., Santhosh, K. D. & Mareeswari, V (2018). Prediction of cardiovascular disease using machine learning algorithms. International Conference on Current Trends towards Converging Technologies. Retrieved from DOI:10.1109/ICCTCT.2018.8550857
  10. Dwivedi, A. K. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing and Applications, 29(10), 685-693.
  11. Galetsi, P., Katsaliaki, K. & Kumar, S. (2019). Values, challenges and future directions of big data analytics in healthcare: A systematic review. Social Science and Medicine, 241(5), 112533.
  12. Galetsi, P. & Katsaliaki, K. (2019). A review of the literature on big data analytics in healthcare. Journal of the Operational Research Society, 71(1), 1-19.
  13. Gao, X., Ali, A. A., Shaban, H. & Anwar, E. M. (2021). Improving the accuracy for analyzing heart diseases prediction based on the ensemble method. Complexity, 1-10. Retrieved from DOI:10.1155/2021/6663455
  14. Karim, A., Jonkman, M., Hasan, M. Z. & Ghosh, P. (2021). Use of efficient machine learning techniques in the identification of patients with heart diseases. ACM International Conference Proceeding Series. Retrieved from DOI: 10.1145/3471287.3471297
  15. Ghosh, P., Azam, S., Jonkman, M. & Karim, A. (2021). Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access. Retrieved from DOI: 10.1109/ACCESS.2021.3053759
  16. Goel, S., Deep, A., Srivastava, S. & Tripathi, A. (2019). Comparative analysis of various techniques for heart disease prediction. 4th International Conference on Information Systems and Computer Networks (ISCON). Retrieved from DOI:10.1109/ISCON47742.2019.9036290
  17. Huang, H., Tan, J. & Hua, D. (2021). Data mining of association between hyperuricemia and common chronic diseases based on evolutionary apriori algorithm (EAA). IEEE 6th International Conference on Cloud Computing and Big Data Analytics. Retrieved from DOI:10.1109/ICCCBDA51879.2021.9442490
  18. Krishnan, S. J. & Geetha, S. (2019). Prediction of heart disease using machine learning algorithms. 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT). Retrieved from DOI:10.1109/ICIICT1.2019.8741465
  19. Modepalli, K., Gnaneswar, G., Dinesh, R. & Sai, Y. R. (2021). Heart disease prediction using hybrid machine Learning model. 6th International Conference on Inventive Computation Technologies (ICICT). Retrieved from DOI:10.1109/ICICT50816.2021.9358597
  20. Kohli, P. S. & Arora, S.  (2018). Application of machine learning in disease prediction. 4th International Conference on Computing Communication and Automation (ICCCA). Retrieved from DOI: 10.1109/CCAA.2018.8777449
  21. Krishnani, D. (2019). Prediction of coronary heart disease using supervised machine learning algorithms. IEEE Region 10 Conference (TENCON). Retrieved from DOI: 10.1109/TENCON.2019.8929434
  22. Kwakye, K. & Dadzie, E. (2021). Machine learning-based classification algorithms for the prediction of coronary heart diseases. Retrieved from _Classification_Algorithms_for_the_Prediction_of_Coronary_Heart_Diseases
  23. Mutyala, N. K., Koushik, K. V & Krishna, K. D. (2018). Prediction of heart diseases using data mining and machine learning algorithms and tools. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. Retrieved from DOI:10.13140/RG.2.2.28488.83203
  24. Li, J., Haq, A., Swati, S. & Khan, A. (2020). Heart disease identification method using machine learning classification in e-healthcare. IEEE Acsess, 107562–107582. Retrieved from DOI: 10.1109/ACCESS.2020.3001149
  25. Pavithra, V. & Jayalakshmi, V. (2021). Comparative study of machine learning classification techniques to predict the cardiovascular diseases using HRFLC. 5th International Conference on Intelligent Computing and Control Systems (ICICCS). Retrieved from DOI:10.1109/ICICCS51141.2021.9432105
  26. Qayyum, A., Qadir, J., Bilal, M. & Al-Fuqaha, A. (2021). Secure and robust machine learning for healthcare: A survey. IEEE Reviews in Biomedical Engineering, 14, 156-180.
  27. Anirban, D. (2021). Prediction of coronary heart diseases using machine learning-based. Liverpool John Moores University.
  28. Sivabalaselvamani, D., Selvakarthi, D., Loganathan, R. & Eswari, S. N. (2021). Investigation on heart disease using machine learning algorithms. International Conference on Computer Communication and Informatics (ICCCI). Retrieved from DOI:10.1109/ICCCI50826.2021.9402390
  29. Rajdhan, A., Agarwal, A., Sai, M. & Ghuli, P. (2020). Heart disease prediction using machine learning. International Journal of Engineering Research & Technology (IJERT), 9(4), 659-662.
  30. Rosengren, A., Smyth, A., … Yusuf. S. (2019). Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: The Prospective Urban Rural Epidemiologic (PURE) study. The Lancet Global Health, 7(6), e748–e760.
  31. Sharma, H. & Rizvi, M. A. (2017). Prediction of heart disease using machine learning algorithms: A survey. International Journal on Recent and Innovation Trends in Computing and Communication, 5(8), 99-104.
Abstract Views: 3
PDF Views: 196

Related Articles
Effective Detection of Heart Disease Symptoms using Machine Learning
Gunji JaiSadhashiva, Shaik Mohammad Mohaboob Shareef, Devarakonda Aditya, Leela Venkat Muppavarapu, Dr. Senthil Athithan, Dr. B. Suneetha
Hashtag investor – Perception Analysis with Relation to Geographical Location in Twitter
Samitha Kolambage, Hasath Tillekeratne, Niroshan Chathuranga, Hasanthi Devendra, Muditha Tissera Prince
Credit Default Prediction System Using Machine Learning
Hassan J. Bature, Daniel D. Wisdom, Tolulope T. Dufuwa, Isaac O. Ayetuoma
Key aspects of Autonomous driving software
Parminder Pal Kaur, Sudhir Kumar
Enhancing Facial Detection and Recognition: Leveraging OpenCV and CNNs for Efficient Analysis
P. Srinadh, B. Sai Pavan, B. Naga Sai Chaitanya, V. Vidyadhar, G. R. Anantha Raman, Senthil Athithan

By continuing to use this website, you consent to the use of cookies in accordance with our Cookie Policy.