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Research Article | Open Access

Deep Neural Networks for Prediction of Card-iovascualr Diseases

Muhammad Aqeel Aslam1,2Muhammad Asif Munir3Rauf Ahmad3Muhammad Samiullah3Nasir Mahmood Hassan4Shahzadi Mahnoor5Daxiang Cui6( )
NInstitute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Instrument for Diagnosis and Therapy, Department of Instrument Science & Engineering, School of Electronic Information and Electrical Engineering, Yantai Information Technology Research Institute of Shanghai Jiao Tong University, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
Electrical Engineering Department, GIFT University, Gujranwala, Pakistan
Electrical Enngineering Department, Swedish College of Engineering & Technology, Rahim Yar Khan, Pakistan
Electrical Engineering Department, Khawaja Fareed University of Engineering & Information Technology, Rahim Yar Khan, Pakistan
Information and Commmunciation Engineering Department, UESTC, Chengdu, China
National Engineering Research Center for Nanotechnology, 28 Jiangchuan Road, Shanghai 200241, China
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Abstract

In recent years, a huge extent of data that contains hidden information is collected by the health care industries. Deep Neural Networks (DNN) have been employed to obtain appropriate decisions and effective results. The obtained results have been validated using confusion matrix and region of interest. In this work, we have used fourteen parameters for the prediction of cardiovascular disease (CVD) of 303 volunteers. The proposed predictive technique predicts that the chance for prediction of the risk level of cardiovascular disease. In this work, the prediction method using deep neural networks showed the highest accuracy. Our proposed method has outperformed the existing methods and can be combined with multimedia technology.

References

[1]

Benjamin, Emelia J. et al. Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation, 2017, 135(10).

[2]
Park, Hyun Woo, et al. A hybrid feature selection method to classification and its application in hypertension diagnosis. International Conference on Information Technology in Bio-and Medical Informatics. Springer, 11-19, Cham, 2017.
[3]
Park, Hyun Woo, et al. Risk factors rule mining in hypertension: Korean national health and nutrient examinations survey 2007–2014. 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 1-4, 2016.
[4]
World Health Organization (WHO). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 01 August 2020
[5]

Raza, Ali, et al. Heartbeat sound signal classification using deep learning. Sensors, 2019, 19(21).

[6]

Hanna, Ibrahim R., and Mark E. Silverman. A history of cardiac auscultation and some of its contributors. The American journal of cardiology, 2002, 90(3): 259-267.

[7]

Chirakanphaisarn, Neramitr, Thadsanee thongkanluang, and Yuwathida Chiwpreechar. Heart rate measurement and electrical pulse signal analysis for subjects span of 20-80 years. Journal of Electrical Systems and Information Technology, 2018, 5(1): 112-120.

[8]

Jiang, Zhongwei, and Samjin Choi. A cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope. Expert Systems with Applications, 2006, 31(2): 286-298.

[9]
National Heart, Lung, and Blood Institute. https://www.nhlbi.nih.gov/health-topics/coronary-heart-disease. Accessed 15 August 2020
[10]
Nucleus Medical Media. http://www.nucleushealth.com/ Accessed 01 Oct 2019
[11]

Hausmann, Harald, et al. Decision-making in end-stage coronary artery disease: revascularization or heart transplantation. The Annals of thoracic surgery, 1997, 64(5): 1296-1302.

[12]

Diamond, George A., and James S. Forrester. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. New England Journal of Medicine, 1979, 300(24): 1350-1358.

[13]

Zafar, Kashan, et al. Skin lesion segmentation from dermoscopic images using convolutional neural network. Sensors, 2020, 20(6): 1601-1614.

[14]

Kim, Hyeongsoo, et al. A data mining approach for cardiovascular disease diagnosis using heart rate variability and images of carotid arteries. Symmetry, 2016, 8(6): 47.

[15]

Kim, Jaekwon, Jongsik Lee, and Youngho Lee. Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthcare Informatics Research, 2015, 21(3): 167-174.

[16]

Kim, Jae Kwon, and Sanggil Kang. Neural network-based coronary heart disease risk prediction using feature correlation analysis. Journal of Healthcare Engineering, 2017, 1-13.

[17]

Olaniyi, Ebenezer Obaloluwa, OyebadeKayodeOyedotun, and Khashman Adnan. Heart diseases diagnosis using neural networks arbitration. International Journal of Intelligent Systems and Applications, 2015, 7(12): 72.

[18]

Haq, Amin Ul, et al. A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Information Systems, 2018: 1-21.

[19]
Shouman, Mai, Tim Turner, and Rob Stocker. Using decision tree for diagnosing heart disease patients. Proceedings of the Ninth Australasian Data Mining Conference-Volume 121. 2011.
[20]

Deekshatulu, B.L., and Priti Chandra. Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology, 2013, 10: 85-94

[21]
Ghumbre S.U., Ghatol A.A., Heart Disease Diagnosis Using Machine Learning Algorithm. In 2012 Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012, 2012: 217-225.
[22]

Sakr, Sherif, et al. Comparison of machine learning techniques to predict all-cause mortality using fitness data: The Henry ford exercise testing (FIT) project. BMC medical informatics and decision making, 2017, 17(1): 174.

[23]

Kourou, Konstantina, et al. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 2015, 13(8-17).

[24]

Cifuentes-Alcobendas, Gabriel, and Manuel Domínguez-Rodrigo. Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks. Scientific Reports, 2019, 9(1): 1-12.

[25]

Aslam, Muhammad Aqeel, et al. Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network. Scientific Report, 2021, 11(1): 1-12.

[26]
Hasan, Tabreer T., Manal H. Jasim, and Ivan A. Hashim. FPGA Design and Hardware Implementation of Heart Disease Diagnosis System Based on NVG-RAM Classifier. 2018 Third Scientific Conference of Electrical Engineering (SCEE). IEEE, 2018.
[27]

Muhammad, Yar, et al. Early and accurate detection and diagnosis of heart disease using intelligent computational model. Scientific Reports, 2020, 10(1): 1-7.

[28]
Abdullah A.S., Rajalaxmi R., A data mining model for predicting the coronary heart disease using random forest classifier. In 2012 International Conference in Recent Trends in Computational Methods, Communication and Controls, 22-25, 2012.
[29]

Srinivas, K., B. Kavihta Rani, and A. Govrdhan. Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2010, 2(2): 250-255.

[30]

Nahar, Jesmin, et al. Computational intelligence for heart disease diagnosis: A medical knowledge driven approach. Expert Systems with Applications, 2013, 40(1): 96-104.

[31]

Chaurasia, Vikas, and Saurabh Pal. Early prediction of heart diseases using data mining techniques. Caribbean Journal of Science and Technology, 2013, 1: 208-217.

[32]

Das, Resul, Ibrahim Turkoglu, and Abdulkadir Sengur. Effective diagnosis of heart disease through neural networks ensembles. Expert Systems with Applications, 2009, 36(4): 7675-7680.

[33]

Yazdani, Armin, et al. A novel approach for heart disease prediction using strength scores with significant predictors. BMC Medical Informatics and Decision Making, 2021, 21(1): 1-16.

[34]

Almustafa, Khaled Mohamad. Prediction of heart disease and classifiers' sensitivity analysis. BMC bioinformatics, 2020, 21(1): 1-18.

[35]

Shah, Devansh, Samir Patel, and Santosh Kumar Bharti. Heart disease prediction using machine learning techniques. SN Computer Science, 2020, 1(6): 1-6.

[36]
Jindal, Harshit, et al. Heart disease prediction using machine learning algorithms. IOP Conference Series: Materials Science and Engineering. 2021, 1022(1), IOP Publishing.
[37]
Bache, K., Lichman, M. : UCI Machine Learning Repository Irvine. University of California, School of Information and Computer Science, Oakland (2013).
[38]

Kadam, V.J., Jadhav S.M., Feature Ensemble Learning Based on Sparse Autoencoders for Diagnosis of Parkinson's Disease. In 2019 Computing, Communication and Signal Processing. Advances in Intelligent Systems and Computing, 2019: 567-581.

[39]

Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.

[40]

Bengio, Yoshua, and Yann Le Cun. Scaling learning algorithms towards AI. Large-scale Kernel Machines, 2007, 34(5): 1-41.

[41]

Ranzato, Marc Aurelio, et al. Efficient learning of sparse representations with an energy-based model. Advances in Neural Information Processing Systems, 2006, 19: 1137-1144.

[42]
Baldi P., Autoencoders, unsupervised learning, and deep architectures. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 2012: 37-50.
[43]

Kittler, Josef, et al. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(3): 226-239.

Nano Biomedicine and Engineering
Pages 81-89
Cite this article:
Aslam MA, Munir MA, Ahmad R, et al. Deep Neural Networks for Prediction of Card-iovascualr Diseases. Nano Biomedicine and Engineering, 2022, 14(1): 81-89. https://doi.org/10.5101/nbe.v14i1.p81-89

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Received: 18 October 2021
Accepted: 08 May 2022
Published: 11 May 2022
© Muhammad Aqeel Aslam, Muhammad Asif Munir, Rauf Ahmad, Muhammad Samiullah, Nasir Mahmood Hassan, Shahzadi Mahnoor, and Daxiang Cui.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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