Early Risk Identification of Cardiac Disease Prediction using Data Mining and Deep Learning Technique


  • S. Tamil Fathima, K. Fathima Bibi


Cardiac disease prediction, Data mining, deep neural network, random forest, decision tree, medical margin rate, recommendation system.


Data Mining (DM) in cardiovascular data prediction is a rapidly growing field of research. As the amount of cardiovascular data available continues to grow, new and more sophisticated data mining. Most peoples affected by the disease without knowing the feature dependencies to make proper treatment leads more deaths. Artificial intelligence techniques make intelligence feature analysis to predict the disease earlier to support treatment. But most of the prevailing techniques are failed to analyses the disease margins and feature release related to disease factor affects the precision rate, so the prediction accuracy is low and to make improper suggestion. To resolve tis properly a novel optimization is need to improve the prediction accuracy based on the deep learning techniques. DM is utilized to extract valuable information from cardiovascular dataset. In this paper, to propose an enhanced deep featured neural network is designed to analyses the cardiac risk to make efficient prediction and recommendation. The Cross Layer Leap Gated Convolution Neural Network (CLLG-CNN) using Recursive Random Forest Feature Selection (RRFFS) for early risk identification is attained for efficient prediction.  The Sparse augmentation disease rate (SADR) finds the ideal margins the feature deficiency factor weight and features are selected using Recursive random forest feature selection (RRFFS). The selected feature are trained with cross layer Leap gated convolution neural network (CLLG-CNN) to find the disease risk factor. The proposed system produce high performance compared to the other system by identifying disease efficiently. This improve the detection rate as well precision recall rate to support from early treatment to avoid the cardiac risks.


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Pal M, Parija S, Panda G, Dhama K, Mohapatra RK. Risk prediction of cardiovascular disease using machine learning classifiers. Open Med (Wars). 2022 Jun 17;17(1):1100-1113. doi: 10.1515/med-2022-0508. PMID: 35799599; PMCID: PMC9206502.

Dalin Zhang" Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing" Volume 2022 | Article ID 1672677 | https://doi.org/10.1155/2022/1672677.

Apurv Garg et al "Heart disease prediction using machine learning techniques" 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1022 012046 DOI 10.1088/1757-899X/1022/1/012046.

Gihyun Lee "Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers" Volume 2017 | Article ID 7483639 | https://doi.org/10.1155/2017/7483639.

Muhammad, Y., Tahir, M., Hayat, M. et al. Early and accurate detection and diagnosis of heart disease using intelligent computational model. Sci Rep 10, 19747 (2020). https://doi.org/10.1038/s41598-020-76635-9.

N. K. S. Banu and S. Swamy, "Prediction of heart disease at early stage using data mining and big data analytics: A survey," 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), Mysuru, India, 2016, pp. 256-261, doi: 10.1109/ICEECCOT.2016.7955226.

Kirmani M. M. Cardiovascular Disease Prediction Using Data Mining Techniques: A Review. Orient. J. Comp. Sci. and Technol, Vol. 10, No. (2), pp. 520-528, 2017.

R Fadnavis, K Dhore, D Gupta, J Waghmare and D Kosankar, “Heart disease prediction using data mining”, International Conference on Research Frontiers in Sciences (ICRFS), 2021, pp.1-7.

M. Swathy and K. Saruladha, "A comparative study of classification and prediction of cardio-vascular diseases (CVD) using machine learning and deep learning techniques", ICT Exp., 2021, [online] Available: https://doi.org/10.1016/j.icte.2021.08.021.

K. Dissanayake and M. G. Md Johar, "Comparative study on heart disease prediction using feature selection techniques on classification algorithms", Appl. Comput. Intell. Soft Comput., vol. 2021, 2021, [online] Available: https://doi.org/10.1155/2021/5581806.

P. E. Rubini, C. A. Subasini, A. V. Katharine, V. Kumaresan, S. G. Kumar and T. M. Nithya, "A cardiovascular disease prediction using machine learning algorithms", Ann. Romanian Soc. Cell Biol., vol. 25, no. 2, pp. 904-912, 2021.

K. F. Alhabib, M. A. Batais, T. H. Almigbal, M. Q. Alshamiri, H. Altaradi, S. Rangarajan, et al., "Demographic behavioral and cardiovascular disease risk factors in the Saudi population: Results from the prospective urban rural epidemiology study (PURE-Saudi)", BMC Public Health, vol. 20, no. 1, pp. 1-14, Dec. 2020.

L. Hu, B. Liu and Y. Li, "Ranking sociodemographic health behavior prevention and environmental factors in predicting neighborhood cardiovascular health: A Bayesian machine learning approach", Preventive Med., vol. 141, Dec. 2020.

C. P. Loizou, E. Kyriacou, M. B. Griffin, A. N. Nicolaides and C. S. Pattichis, "Association of Intima-Media Texture With Prevalence of Clinical Cardiovascular Disease," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 68, no. 9, pp. 3017-3026, Sept. 2021, doi: 10.1109/TUFFC.2021.3081137.

Y. An, K. Tang and J. Wang, "Time-Aware Multi-Type Data Fusion Representation Learning Framework for Risk Prediction of Cardiovascular Diseases," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 6, pp. 3725-3734, 1 Nov.-Dec. 2022, doi: 10.1109/TCBB.2021.3118418.

E. Longato, G. P. Fadini, G. Sparacino, A. Avogaro, L. Tramontan and B. Di Camillo, "A Deep Learning Approach to Predict Diabetes’ Cardiovascular Complications From Administrative Claims," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3608-3617, Sept. 2021, doi: 10.1109/JBHI.2021.3065756.

S. B. Shuvo, S. N. Ali, S. I. Swapnil, M. S. Al-Rakhami and A. Gumaei, "CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings," in IEEE Access, vol. 9, pp. 36955-36967, 2021, doi: 10.1109/ACCESS.2021.3063129.

S. E. A. Ashri, M. M. El-Gayar and E. M. El-Daydamony, "HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm," in IEEE Access, vol. 9, pp. 146797-146809, 2021, doi: 10.1109/ACCESS.2021.3122789.

M. B. Abubaker and B. Babayiğit, "Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods," in IEEE Transactions on Artificial Intelligence, vol. 4, no. 2, pp. 373-382, April 2023, doi: 10.1109/TAI.2022.3159505.

Rahim, Y. Rasheed, F. Azam, M. W. Anwar, M. A. Rahim and A. W. Muzaffar, "An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases," in IEEE Access, vol. 9, pp. 106575-106588, 2021, doi: 10.1109/ACCESS.2021.3098688.

K. Zarkogianni, M. Athanasiou, A. C. Thanopoulou and K. S. Nikita, "Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication," in IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1637-1647, Sept. 2018, doi: 10.1109/JBHI.2017.2765639.

Y. An, N. Huang, X. Chen, F. Wu and J. Wang, "High-Risk Prediction of Cardiovascular Diseases via Attention-Based Deep Neural Networks," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 3, pp. 1093-1105, 1 May-June 2021, doi: 10.1109/TCBB.2019.2935059.

J. Wang et al., "Detecting Cardiovascular Disease from Mammograms With Deep Learning," in IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1172-1181, May 2017, doi: 10.1109/TMI.2017.2655486.

H. R. H. Al-Absi, M. A. Refaee, A. U. Rehman, M. T. Islam, S. B. Belhaouari and T. Alam, "Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study," in IEEE Access, vol. 9, pp. 29929-29941, 2021, doi: 10.1109/ACCESS.2021.3059469.

G. Joo, Y. Song, H. Im and J. Park, "Clinical Implication of Machine Learning in Predicting the Occurrence of Cardiovascular Disease Using Big Data (Nationwide Cohort Data in Korea)," in IEEE Access, vol. 8, pp. 157643-157653, 2020, doi: 10.1109/ACCESS.2020.3015757.

P. Ghosh et al., "Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques," in IEEE Access, vol. 9, pp. 19304-19326, 2021, doi: 10.1109/ACCESS.2021.3053759.

D. Xu, J. Q. Sheng, P. J. -H. Hu, T. -S. Huang and C. -C. Hsu, "A Deep Learning–Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 2260-2272, June 2021, doi: 10.1109/JBHI.2020.3033323.

D. Padovano, A. Martinez-Rodrigo, J. M. Pastor, J. J. Rieta and R. Alcaraz, "On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning," in IEEE Access, vol. 10, pp. 92710-92725, 2022, doi: 10.1109/ACCESS.2022.3201911.

Z. Hu, H. Qiu, Z. Su, M. Shen and Z. Chen, "A Stacking Ensemble Model to Predict Daily Number of Hospital Admissions for Cardiovascular Diseases," in IEEE Access, vol. 8, pp. 138719-138729, 2020, doi: 10.1109/ACCESS.2020.3012143.

M. Blanchard, M. Feuilloy, A. Sabil, C. Gervès-Pinquié, F. Gagnadoux and J. -M. Girault, "A Deep Survival Learning Approach for Cardiovascular Risk Estimation in Patients With Sleep Apnea," in IEEE Access, vol. 10, pp. 133468-133478, 2022, doi: 10.1109/ACCESS.2022.3231743.

Abdellatif, H. Abdellatef, J. Kanesan, C. -O. Chow, J. H. Chuah and H. M. Gheni, "An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods," in IEEE Access, vol. 10, pp. 79974-79985, 2022, doi: 10.1109/ACCESS.2022.3191669.

M. Alkhodari, D. K. Islayem, F. A. Alskafi and A. H. Khandoker, "Predicting Hypertensive Patients With Higher Risk of Developing Vascular Events Using Heart Rate Variability and Machine Learning," in IEEE Access, vol. 8, pp. 192727-192739, 2020, doi: 10.1109/ACCESS.2020.3033004.

Q. A. Rahman, L. G. Tereshchenko, M. Kongkatong, T. Abraham, M. R. Abraham and H. Shatkay, "Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification," in IEEE Transactions on NanoBioscience, vol. 14, no. 5, pp. 505-512, July 2015, doi: 10.1109/TNB.2015.2426213.

Bárbara Martins, Diana Ferreira, Cristiana Neto, António Abelha, José Machado “Data Mining for Cardiovascular Disease Prediction” Journal of Medical SystemsVolume 45Issue 1Jan 2021https://doi.org/10.1007/s10916-020-01682-8.

Krishnaiah, V., Narsimha, G., Chandra, N.S. (2015). Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_42.

S. Mohan, C. Thirumalai and G. Srivastava, "Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques," in IEEE Access, vol. 7, pp. 81542-81554, 2019, doi: 10.1109/ACCESS.2019.2923707.

Ishaq et al., "Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques," in IEEE Access, vol. 9, pp. 39707-39716, 2021, doi: 10.1109/ACCESS.2021.3064084.




How to Cite

S. Tamil Fathima. (2024). Early Risk Identification of Cardiac Disease Prediction using Data Mining and Deep Learning Technique . International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2500–2515. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5854



Research Article