Abstract
Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the patients prevent it, but I can also help the medical practitioners learn the major causes of a heart attack and avoid it before its actual occurrence in patient. In this paper, we propose a method named CardioHelp which predicts the probability of the presence of cardiovascular disease in a patient by incorporating a deep learning algorithm called convolutional neural networks (CNN). The proposed method is concerned with temporal data modeling by utilizing CNN for HF prediction at its earliest stage. We prepared the heart disease dataset and compared the results with state-of-the-art methods and achieved good results. Experimental results show that the proposed method outperforms the existing methods in terms of performance evaluation metrics. The achieved accuracy of the proposed method is 97%.
Similar content being viewed by others
References
Ueshima, H.; et al.: Cardiovascular disease and risk factors in Asia: a selected review. Circulation 118(25), 2702–2709, 2008. https://doi.org/10.1161/CIRCULATIONAHA.108.790048.
Bewu, A.; Mbanya, J.C.: Cardiovascular Disease. In: Jamison, D.T;, Feachem, R.G.; Makgoba, M.W.; et al. editors. Disease and Mortality in Sub-Saharan Africa. 2nd edition. Washington (DC): The International Bank for Reconstruction and Development/The World Bank; 2006. Chapter 21. https://www.ncbi.nlm.nih.gov/books/NBK2294/ (2006)
Ramirez-Bautista, J.A.; Hernández-Zavala, A.; Chaparro-Cárdenas, S.L.; Huerta-Ruelas, J.A.: Review on plantar data analysis for disease diagnosis. Biocybern. Biomed. Eng. 38, 342–361, 2018
Nalluri, S.; Saraswathi, R.V.; Ramasubbareddy, S.; Govinda, K.; Swetha, E.: Chronic heart disease prediction using data mining techniques. In: Data Engineering and Communication Technology, ed: Springer, pp. 903–912 (2020)
(2017, 25 March). WHO: World Health Organization, Media Centre, cardiovascular diseases fact sheet webpage. https://www.who.int/mediacentre/factsheets/fs317/en/ (2017)
Sharma, R.; Agarwal, P.; Mahapatra, R.P.: Evolution in big data analytics on internet of things: applications and future plan. In: Multimedia Big Data Computing for IoT Applications, ed: Springer, pp. 453–477 (2020)
Gartner: The Importance of 'Big Data': A Definition. https://www.gartner.com/doc/2057415/importance-big-data-definition (2012)
Jordan, M.I.; Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349, 255–260, 2015
Gupta, R.; Khari, M.; Gupta, D.; Crespo, R.G.: Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Information Sciences (2020)
Gupta, R.; Khari, M.; Gupta, V.; Verdú, E.; Wu, X.; Herrera-Viedma, E.; González Crespo, R.: Fast single image haze removal method for inhomogeneous environment using variable scattering coefficient. Comput. Model. Eng. Sci. 123(3), 1175–1192, 2020
LeCun, Y.; Bengio, Y.; Hinton, G.: Deep learning. Nature 521, 436–444, 2015
Khari, M.; Garg, A.K.; Crespo, R.G.; Verdú, E.: Gesture recognition of RGB and RGB-D static images using convolutional neural networks. Int. J. Interact. Multimed. Artif. Intell. 5(7) (2019)
Robinson, Y.H.; Vimal, S.; Khari, M.; Hernández, F.C.L.; Crespo, R.G.: Tree-based convolutional neural networks for object classification in segmented satellite images. Int. J. High Perform. Comput. Appl. 1094342020945026 (2020)
Dua, M.; Gupta, R.; Khari, M.; Crespo, R.G.: Biometric iris recognition using radial basis function neural network. Soft. Comput. 23(22), 11801–11815, 2019
Esteva, A.; Robicquet, A.; Ramsundar, B.; Kuleshov, V.; DePristo, M.; Chou, K.; et al.: A guide to deep learning in healthcare. Nat. Med. 25, 24–29, 2019
Rathnayakc, B.S.S.; Ganegoda, G.U.: Heart diseases prediction with data mining and neural network techniques. In: 2018 3rd International Conference for Convergence in Technology (I2CT), pp. 1–6 (2018)
Van Pham, H.: A proposal of expert system using deep learning neural networks and fuzzy rules for diagnosing heart disease. In: Frontiers in Intelligent Computing: Theory and Applications, ed: Springer, pp. 189–198 (2020)
Qrenawi, M.I.; Al Sarraj, W.: Identification of cardiovascular diseases risk factors among diabetes patients using ontological data mining techniques. In: 2018 International Conference on Promising Electronic Technologies (ICPET), pp. 129–134 (2018)
Nguyen, T.-H.; Nguyen, T.-N.; Nguyen, T.-T.: A deep learning framework for heart disease classification in an IoTs-based system. In: A Handbook of Internet of Things in Biomedical and Cyber Physical System, ed: Springer, pp. 217–244 (2020)
Isra’a Ahmed Zriqat, A.M.; Altamimi, M.A.: A comparative study for predicting heart diseases using data mining classification methods
More, K.; Raihan, M.; More, A.; Padule, S.; Mondal, S.: A12176 Smart phone based “heart attack” risk prediction; innovation of clinical and social approach for preventive cardiac health. J. Hypertens. 36, e321, 2018
Aldallal, A.; Al-Moosa, A.A.A.: Using data mining techniques to predict diabetes and heart diseases
O’Donnell, J.; Velardo, C.; Shah, S.A.; Khorshidi, G.S.; Salvi, D.; Rahimi, K.; et al.: Physical activity and sleep analysis of heart failure patients using multi-sensor patches. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6092–6095 (2018)
Awan, S.M.; Riaz, M.U.; Khan, A.G.: Prediction of heart disease using artificial neural network. VFAST Trans. Softw. Eng. 13, 102–112, 2018
Alizadeh-dizaj, G.: Risk Prediction and Stratification of Patients with Stroke Using Data Mining Techniques. Tabriz University of Medical Sciences, School of Management and Medical Informatics (2018)
Somasundaram, T.J.P.K.: An empirical study on prediction of heart disease using classification data mining techniques, presented at the IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012), (2015)
Liu, Y.: Environmental Health Nexus: Designing Predictive Models for Improving Public Health Interventions (2018)
Deepika, M.; Kalaiselvi, K.: A empirical study on disease diagnosis using data mining techniques. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 615–620 (2018)
Malav, A.; Kadam, K.: A Hybrid Approach for Heart Disease Prediction Using Artificial Neural Network and K-means
Wankhar, I.; Wriang, I.; Debnath, P.; Bordoloi, P.; Tripathi, R.; Bhatia, D.; et al.: Comparison and Performance Evaluation of ECG Classification Techniques Trained with Shorter Database, ed: IJSRST, (2018)
Ordonez, C.: Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans. Inf. Technol. Biomed. 10, 334–343, 2006
Xing, Y.; Wang, J.; Zhao, Z.: Combination data mining methods with new medical data to predicting outcome of coronary heart disease. In: 2007 International Conference on Convergence Information Technology (ICCIT 2007), pp. 868–872 (2007)
Srinivas, K.; Rao, G.R.; Govardhan, A.: Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques. In: 2010 5th International Conference on Computer Science & Education, pp. 1344–1349 (2010)
Liu, J.-L.; Hsu, Y.-T.; Hung, C.-L.: Development of evolutionary data mining algorithms and their applications to cardiac disease diagnosis. IEEE Congress on Evolutionary Computation 2012, 1–8, 2012
Chandra, P.; Deekshatulu, B.: Prediction of risk score for heart disease using associative classification and hybrid feature subset selection. In: 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 628–634 (2012)
Amin, S.U.; Agarwal, K.; Beg, R.: Genetic neural network based data mining in prediction of heart disease using risk factors. In: 2013 IEEE Conference on Information & Communication Technologies, pp. 1227–1231 (2013)
Maji, S.; Arora, S.: Decision Tree Algorithms for Prediction of Heart Disease. Information and Communication Technology for Competitive Strategies, pp. 447–454. Springer, Singapore (2019)
Alim, M.A.; et al.: Robust heart disease prediction: a novel approach based on significant feature and ensemble learning model. In: 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, (2020)
Mienye, I.D.; Yanxia, S.; Zenghui, W.: Improved sparse autoencoder based artificial neural network approach for prediction of heart disease. Informatics in Medicine Unlocked, 100307 (2020)
Dwivedi, A.K.: Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput. Appl. 29(10), 685–693, 2018
https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/
LeCun, Y.; Bengio, Y.: Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Netw. 3361, 1995, 1995
Iandola, F.: Exploring the design space of deep convolutional neural networks at large scale, arXiv preprint arXiv:1612.06519 (2016)
Szegedy, C.; Ioffe, S.; Vanhoucke, V.; Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence, (2017)
He, K.; Zhang, X.; Ren, S.; Sun, J.: Deep residual learning for image recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, ed: IEEE (2016)
Krizhevsky, A.; Sutskever, I.; Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)
Radford, A.; Metz, L.; Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Author information
Authors and Affiliations
Contributions
All the authors contributed equally to this work.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
Not applicable.
Rights and permissions
About this article
Cite this article
Mehmood, A., Iqbal, M., Mehmood, Z. et al. Prediction of Heart Disease Using Deep Convolutional Neural Networks. Arab J Sci Eng 46, 3409–3422 (2021). https://doi.org/10.1007/s13369-020-05105-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-020-05105-1