An Accurate Prediction Framework for Cardiovascular Disease Using Convolutional Neural Networks

Document Type : Research Paper


1 Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, Telangana, India.

2 Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu, India.



Cardiovascular-Diseases (CVD) are a principal cause of death worldwide. According to the World-Health-Organization (WHO), cardiovascular illnesses kill 20 million people annually. Predictions of heart-disease can save lives or take them, depending on how precise they are. The virus has rendered conventional methods of disease anticipation ineffective. Therefore, a unified system for accurate illness prediction is required. The study of disease diagnosis and identification has reached new heights thanks to artificial intelligence. With the right kind of training and testing, deep learning has quickly become one of the most cutting-edge, reliable, and sustaining technologies in the field of medicine. Using the University of California Irvine (UCI) machine-learning (ML) heart disease dataset, we propose a Convolutional-Neural-Network (CNN) for early disease prediction. There are 14 primary characteristics of the dataset that are being analyzed here. Accuracy and confusion matrix are utilized to verify several encouraging outcomes. Irrelevant features in the dataset are eliminated utilizing Isolation Forest, and the data is also standardized to enhance accuracy. Accuracy of 98% was achieved by employing a deep learning technique.


Ahmed, S. T. (2017, June). A study on multi objective optimal clustering techniques for medical datasets. In 2017 international conference on intelligent computing and control systems (ICICCS) (pp. 174-177). IEEE.
Ahmed, S. T., Kumar, V. V., Singh, K. K., Singh, A., Muthukumaran, V., & Gupta, D. (2022). 6G enabled federated learning for secure IoMT resource recommendation and propagation analysis. Computers and Electrical Engineering102, 108210.
Alizadeh, F., Morell, E., Hummel, K., Wu, Y., Wypij, D., Matthew, D. & Blume, E. D. (2022). The surprise question as a trigger for primary palliative care interventions for children with advanced heart disease. Pediatric Cardiology43(8), 1822-1831.
Almustafa, K. M. (2020). Prediction of heart disease and classifiers’ sensitivity analysis. BMC bioinformatics21(1), 1-18.
Ansarullah, S. I., Saif, S. M., Kumar, P., & Kirmani, M. M. (2022). Significance of visible non-invasive risk attributes for the initial prediction of heart disease using different machine learning techniques. Computational intelligence and neuroscience2022.
Ashreetha, B., Devi, M. R., Kumar, U. P., Mani, M. K., Sahu, D. N., & Reddy, P. C. S. (2022). Soft optimization techniques for automatic liver cancer detection in abdominal liver images. International journal of health sciences6.
Baskar, S., Nandhini, I., Prasad, M. L., Katale, T., Sharma, N., & Reddy, P. C. S. (2023, November). An Accurate Prediction and Diagnosis of Alzheimer’s Disease using Deep Learning. In 2023 IEEE North Karnataka Subsection Flagship International Conference (NKCon) (pp. 1-7). IEEE.
Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational intelligence and neuroscience2021.
Budholiya, K., Shrivastava, S. K., & Sharma, V. (2022). An optimized XGBoost based diagnostic system for effective prediction of heart disease. Journal of King Saud University-Computer and Information Sciences34(7), 4514-4523.
Chaurasia, D. V., & Pal, S. (2013). Early prediction of heart diseases using data mining techniques. Caribbean journal of Science and Technology1, 208-217.
Chitra, S., & Jayalakshmi, V. (2022). Prediction of heart disease and chronic kidney disease based on internet of things using RNN algorithm. In Proceedings of Data Analytics and Management: ICDAM 2021, Volume 1 (pp. 467-479). Springer Singapore.
Doppala, B. P., Bhattacharyya, D., Janarthanan, M., & Baik, N. (2022). A reliable machine intelligence model for accurate identification of cardiovascular diseases using ensemble techniques. Journal of Healthcare Engineering2022.
El-Hasnony, I. M., Elzeki, O. M., Alshehri, A., & Salem, H. (2022). Multi-label active learning-based machine learning model for heart disease prediction. Sensors22(3), 1184.
Faieq, A. K., & Mijwil, M. M. (2022). Prediction of heart diseases utilising support vector machine and artificial neural network. Indonesian Journal of Electrical Engineering and Computer Science26(1), 374-380.
Gavhane, A., Kokkula, G., Pandya, I., & Devadkar, K. (2018, March). Prediction of heart disease using machine learning. In 2018 second international conference on electronics, communication and aerospace technology (ICECA) (pp. 1275-1278). IEEE.
Huang, J., Li, Z., Zhang, W., Lv, Z., Dong, S., Feng, Y. & Zhao, Y. (2023). Explainable machine learning-assisted origin identification: Chemical profiling of five lotus (Nelumbo nucifera Gaertn.) parts. Food Chemistry404, 134517.
Kumar, K., Pande, S. V., Kumar, T., Saini, P., Chaturvedi, A., Reddy, P. C. S., & Shah, K. B. (2023). Intelligent controller design and fault prediction using machine learning model. International Transactions on Electrical Energy Systems2023.
Latha, C. B. C., & Jeeva, S. C. (2019). Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked16, 100203.
LK, S. S., Ahmed, S. T., Anitha, K., & Pushpa, M. K. (2021, November). COVID-19 outbreak based coronary heart diseases (CHD) prediction using SVM and risk factor validation. In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) (pp. 1-5). IEEE.
Lokesh, S., Priya, A., Sakhare, D. T., Devi, R. M., Sahu, D. N., & Reddy, P. C. S. (2022). CNN based deep learning methods for precise analysis of cardiac arrhythmias. International journal of health sciences6.
Mamatha, B., Rashmi, D., Tiwari, K. S., Sikrant, P. A., Jovith, A. A., & Reddy, P. C. S. (2023, August). Lung Cancer Prediction from CT Images and using Deep Learning Techniques. In 2023 Second International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON) (pp. 263-267). IEEE.
Manimurugan, S., Almutairi, S., Aborokbah, M. M., Narmatha, C., Ganesan, S., Chilamkurti, N., & Almoamari, H. (2022). Two-stage classification model for the prediction of heart disease using IoMT and artificial intelligence. Sensors22(2), 476.
Muser, D., Liang, J. J., Castro, S. A., Lanera, C., Enriquez, A., Kuo, L. & Santangeli, P. (2019). Performance of prognostic heart failure models in patients with nonischemic cardiomyopathy undergoing ventricular tachycardia ablation. JACC: Clinical Electrophysiology5(7), 801-813.
Muthappa, K. A., Nisha, A. S. A., Shastri, R., Avasthi, V., & Reddy, P. C. S. (2023). Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs. Applied Nanoscience, 1-10.
Nancy, A. A., Ravindran, D., Raj Vincent, P. D., Srinivasan, K., & Gutierrez Reina, D. (2022). Iot-cloud-based smart healthcare monitoring system for heart disease prediction via deep learning. Electronics11(15), 2292.
Reddy, P. C. S., Sucharitha, Y., & Narayana, G. S. (2021). Forecasting of Covid-19 Virus Spread Using Machine Learning Algorithm. International Journal of Biology and Biomedicine6.
Riyaz, L., Butt, M.A., Zaman, M. and Ayob, O., 2022. Heart disease prediction using machine learning techniques: a quantitative review. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2021, Volume 3 (pp. 81-94). Springer Singapore.
Sampath, S., Parameswari, R., Prasad, M. L., Kumar, D. A., Hussain, M. M., & Reddy, P. C. S. (2023, December). Ensemble Nonlinear Machine Learning Model for Chronic Kidney Diseases Prediction. In 2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
Saqlain, S. M., Sher, M., Shah, F. A., Khan, I., Ashraf, M. U., Awais, M., & Ghani, A. (2019). Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowledge and Information Systems58, 139-167.
Shanmugaraja, P., Bhardwaj, M., Mehbodniya, A., VALI, S., & Reddy, P. C. S. (2023). An Efficient Clustered M-path Sinkhole Attack Detection (MSAD) Algorithm for Wireless Sensor Networks. Adhoc & Sensor Wireless Networks55.
Song, C., Liu, B., Yang, D., Diao, H., Zhao, L., Lu, Y. & Zhang, X. (2015). Association between interleukin-6 gene− 572G> C polymorphism and coronary heart disease. Cell biochemistry and biophysics71, 359-365.
Suneel, S., Balaram, A., Amina Begum, M., Umapathy, K., Reddy, P. C. S., & Talasila, V. (2024). Quantum mesh neural network model in precise image diagnosing. Optical and Quantum Electronics, 56(4), 559.