Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 4/2021

03-01-2021 | Research Article-Computer Engineering and Computer Science

Prediction of Heart Disease Using Deep Convolutional Neural Networks

Authors: Awais Mehmood, Munwar Iqbal, Zahid Mehmood, Aun Irtaza, Marriam Nawaz, Tahira Nazir, Momina Masood

Published in: Arabian Journal for Science and Engineering | Issue 4/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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%.
previous article Probabilistic Neural Network-based Model for Identification of Parkinson’s Disease by using Voice Profile and Personal Data
next article Multivariate Time Series Forecasting with Dilated Residual Convolutional Neural Networks for Urban Air Quality Prediction

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
2.
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)
3.
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, 2018CrossRef
4.
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)
5.
6.
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)
7.
8.
9.
Gupta, R.; Khari, M.; Gupta, D.; Crespo, R.G.: Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Information Sciences (2020)
10.
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
11.
LeCun, Y.; Bengio, Y.; Hinton, G.: Deep learning. Nature 521, 436–444, 2015CrossRef
12.
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)
13.
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)
14.
Dua, M.; Gupta, R.; Khari, M.; Crespo, R.G.: Biometric iris recognition using radial basis function neural network. Soft. Comput. 23(22), 11801–11815, 2019CrossRef
15.
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, 2019CrossRef
16.
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)
17.
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)
18.
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)
19.
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)
20.
Isra’a Ahmed Zriqat, A.M.; Altamimi, M.A.: A comparative study for predicting heart diseases using data mining classification methods
21.
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, 2018CrossRef
22.
Aldallal, A.; Al-Moosa, A.A.A.: Using data mining techniques to predict diabetes and heart diseases
23.
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)
24.
Awan, S.M.; Riaz, M.U.; Khan, A.G.: Prediction of heart disease using artificial neural network. VFAST Trans. Softw. Eng. 13, 102–112, 2018CrossRef
25.
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)
26.
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)
27.
Liu, Y.: Environmental Health Nexus: Designing Predictive Models for Improving Public Health Interventions (2018)
28.
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)
29.
Malav, A.; Kadam, K.: A Hybrid Approach for Heart Disease Prediction Using Artificial Neural Network and K-means
30.
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)
31.
Ordonez, C.: Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans. Inf. Technol. Biomed. 10, 334–343, 2006CrossRef
32.
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)
33.
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)
34.
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
35.
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)
36.
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)
37.
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)CrossRef
38.
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)
39.
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)
40.
Dwivedi, A.K.: Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput. Appl. 29(10), 685–693, 2018CrossRef
41.
42.
LeCun, Y.; Bengio, Y.: Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Netw. 3361, 1995, 1995
43.
44.
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)
45.
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)
46.
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)
47.
Radford, A.; Metz, L.; Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:​1511.​06434 (2015)
Metadata
Title
Prediction of Heart Disease Using Deep Convolutional Neural Networks
Authors
Awais Mehmood
Munwar Iqbal
Zahid Mehmood
Aun Irtaza
Marriam Nawaz
Tahira Nazir
Momina Masood
Publication date
03-01-2021
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 4/2021
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-020-05105-1

Other articles of this Issue 4/2021

Arabian Journal for Science and Engineering 4/2021 Go to the issue

Research Article-Computer Engineering and Computer Science

Sparse to Dense Scale Prediction for Crowd Couting in High Density Crowds

Research Article-Computer Engineering and Computer Science

Training Feedforward Neural Network Using Enhanced Black Hole Algorithm: A Case Study on COVID-19 Related ACE2 Gene Expression Classification

Research Article-Computer Engineering and Computer Science

The Applications of Nature-Inspired Algorithms in Logistic Domains: A Comprehensive and Systematic Review

Research Article-Computer Engineering and Computer Science

Input–Output Modeling and Multi-objective Optimization of Weld Attributes in EBW

Research Article-Computer Engineering and Computer Science

An IoT-Based School Bus and Vehicle Tracking System Using RFID Technology and Mobile Data Networks

Research Article-Computer Engineering and Computer Science

A Deep Learning Framework for Automatic Detection of Hate Speech Embedded in Arabic Tweets

Premium Partners

    Image Credits