International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning Techniques for Heart Disease Prediction |
|---|---|
| ABSTRACT | Heart disease is one of the major causes of death around the world. Therefore, the prediction and diagnosis of heart disease are extremely important. With the increasing amount of data in the healthcare domain, the traditional approaches are no longer effective enough to give accurate and precise results. In the past few years, machine learning and deep learning approaches have been used for the prediction of heart disease. Various machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machines, and Neural Networks have been used for the prediction of heart disease. In recent research studies, the authors have also used hybrid approaches and deep learning approaches along with the Internet of Things for the prediction of heart disease. This literature survey discusses and analyzes the machine learning approaches for the prediction of heart disease based on the research studies conducted in the past five years. In addition, the performance of different machine learning models is measured based on certain factors, including accuracy, precision, recall, and F1 score. The significance of data preprocessing, including feature selection and dealing with class imbalance, is also discussed in the study, which helps in the prediction process. It can be concluded that the survey gives a comprehensive idea about the recent developments in heart disease prediction, which can be used for the development of efficient healthcare systems. |
| AUTHOR | R. PRABHU, M. KRISHNA KUMAR, S. VARSHA, S. G. ABARNA, P.SAMAYAN Assistant Professor, Department of Artificial Intelligence and Data Science, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India Assistant Professor, Department of Mathematics, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India UG Student, Department of Artificial Intelligence and Data Science, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403092 |
| pdf/92_Machine Learning Techniques for Heart Disease Prediction.pdf | |
| KEYWORDS | |
| References | 1. Sangle, P. S., Goudar, R. M., & Bhute, A. N. (2020). Methodologies and techniques for heart disease classification and prediction. ICCCNT Conference. https://doi.org/10.1109/ICCCNT49239.2020.9225673 2. Bharti, R., et al. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational Intelligence and Neuroscience . https://doi.org/10.1155/2021/8387680 3. M. Karuppasamy, M. Jansi Rani, and M. Prabha, An Efficient Resource Allocation Mechanism Using Intelligent Scheduling for Managing Energy in Cloud Computing Infrastructure, Information and Communication Technology for Competitive Strategies (ICTCS 2021), Lecture Notes in Networks and Systems 401 (2023), 81-86. 4. Metaheuristic Feature Selection for Diabetes Prediction with P-G-S Approach, Karuppasamy M, Jansi Rani M, Poorani K, 4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, Procedia Computer Science 252 (2025) 165–171. 5. M. Karuppasamy, M. Prabha, and M. Jansi Rani, Future Worth: Predicting Resale Values with Machine Learning Techniques, Inventive Communication and Computational Technologies, Lecture Notes in Networks and Systems 757 (2023), 1101-1112. 6. M. Jansi Rani, M. Karuppasamy, M. Prabha, and K. Pooran, Detection of COVID-19 CoronaVirus Using ResNet Deep Learning Technique, Signal Processing, Telecommunication and Embedded Systems with AI and ML Applications, Lecture Notes in Electrical Engineering 1281 (2025), 71-83. 7. Hussain, S., et al. (2021). Novel deep learning architecture for heart disease prediction using CNN. https://arxiv.org/abs/2105.10816 8. Xu, M. (2023). Heart disease prediction based on machine learning algorithms. Applied and Computational Engineering. https://doi.org/10.54254/2755-2721/6/20230959 9. Patil, S. S., et al. (2023). Heart disease prediction using machine learning. IJSRST. https://doi.org/10.32628/IJSRST52310382 10. Singh, S., Singh, A., & Limkar, S. (2023). Prediction of heart disease using deep learning and Internet of Medical Things. IJISAE. https://www.ijisae.org/index.php/IJISAE/article/view/3488 11. Patil, R. S., et al. (2023). Hybrid machine learning technique for heart disease prediction. IJISAE . https://ijisae.org/index.php/IJISAE/article/view/2815 12. Bhatt, C. M., et al. (2023). Effective heart disease prediction using machine learning techniques. Algorithms . https://doi.org/10.3390/a16020088 13. Ingole, B. S., et al. (2024). Advancements in heart disease prediction: A machine learning approach . https://arxiv.org/abs/2410.14738 14. Azimi, A., et al. (2025). A comprehensive machine learning framework for heart disease prediction . https://arxiv.org/abs/2505.09969 |
