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Home»Articles»Improved Heart Disease Prediction Using Deep Neural Network

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Improved Heart Disease Prediction Using Deep Neural Network

Author : Mohd Ashraf, M. A. Rizvi and Himanshu Sharma
Volume 8 No.2 April-June 2019 pp 49-54

Abstract

Heart disease is biggest challenge for medical professionals. Modern life style made it an epidemic; according to a survey conducted by WHO heart attack is leading cause of death all over the world especially in the western world. It is surveyed that 23% of the death in US is due to Heart related disease [1]. It has been observed assistance is needed for helping medical professionals in detecting the chance of heart attack in the human. In recent times a lot of work related to providing an automated support system for predicting chance of Heart attack in human has been done. After advancement of computer science, researchers felt that they can help in some of the key interdisciplinary areas like medical science. Machine learning techniques are compared on the single data set which does not reflect true potential of any algorithms. They also suffer from some of the key anomalies such as accuracy and manual data set pre-processing. In this paper, we propose Deep Neural Network methods for creating an automated system for heart attack prediction. It is tested on multiple dataset to find out true potential and providing certainty in the accuracy. Method also promises to remove all the mentioned anomalies from the system like lack of accuracy and automated approach in pre- processing of the data set. In result analysis, it has been observed that prediction is much more efficient and minimum accuracy achieved through this proposed method is 87.64% on any of the data set taken under consideration.

Keywords

Deep Neural Network, Heart Attack, Machine Learning

Full Text:

References

[1] E. O. Olaniyi, O. K. Oyedotun, and K. Adnan, “Heart Diseases Diagnosis Using Neural Networks Arbitration”,Int. J. Intell. Syst. Appl., Vol. 7, No. 12, pp. 75–82, 2015.
[2] G. Parthiban, “Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients”,International Journal of Applied Information Systems (IJAIS)Vol. 3, No. 7, pp. 25–30, 2012.
[3] Yuming Hua, Junhai Guo, and Hua Zhao, “Deep Belief Networks and deep learning”,Proc. 2015 Int. Conf. Intell. Comput. Internet Things, pp. 1–4.
[4] J. Schmidhuber, “Deep Learning in neural networks: An overview”, Neural Networks, Vol. 61, pp. 85-117, Jan 2015.
[5] T. A. Lasko, J. C. Denny, and M. A. Levy, “Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data”,PLoS One, Vol. 8, No. 6, 2013.
[6] P. De, “Modified Random Forest Approach for Resource Allocation in 5G Network”,Int. J. Adv. Comput. Sci. Appl.,Vol. 7, No. 11, pp. 405–413, 2016.
[7] S. Hochreiter and J. UrgenSchmidhuber, “Long Short-Term Memory”,Neural Compuational., Vol. 9, No. 8, pp. 1735–1780, 1997.
[8] S. Palaniappan and R. Awang, “Intelligent heart disease prediction system using data mining techniques”,2008 IEEE/ACS Int. Conf. Comput. Syst. Appl., pp. 108–115, 2008.
[9] P. N. Druzhkov and V. D. Kustikova, “A survey of deep learning methods and software tools for image classification and object detection”,Pattern Recognit. Image Anal., Vol. 26, No. 1, pp. 9–15, 2016.
[10] M. Sultana, A. Haider, and M. S. Uddin, “Analysis of data mining techniques for heart disease prediction”, 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2016 The IEEE website. [Online] Available: http://www.ieee.org/
[11] D. K. Srivastava and L. Bhambhu, “Data classification using support vector machine”,J. Theor. Appl. Inf. Technol., pp. 1-6,2009.
[12] M. A. Jabbar, P. Chandra, and B. L. Deekshatulu, “Prediction of risk score for heart disease using associative classification and hybrid feature subset selection”,Int. Conf. Intell. Syst. Des. Appl. ISDA, pp. 628–634, 2012.
[13] Bo Pang, Lillian Lee and ShivkumarVaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques”, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, Vol. 10, pp. 79-86, 2002.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

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Heart disease is biggest challenge for medical professionals. Modern life style made it an epidemic; according to a survey conducted by WHO heart attack is leading cause of death all over the world especially in the western world. It is surveyed that 23% of the death in US is due to Heart related disease [1]. It has been observed assistance is needed for helping medical professionals in detecting the chance of heart attack in the human. In recent times a lot of work related to providing an automated support system for predicting chance of Heart attack in human has been done. After advancement of computer science, researchers felt that they can help in some of the key interdisciplinary areas like medical science. Machine learning techniques are compared on the single data set which does not reflect true potential of any algorithms. They also suffer from some of the key anomalies such as accuracy and manual data set pre-processing. In this paper, we propose Deep Neural Network methods for creating an automated system for heart attack prediction. It is tested on multiple dataset to find out true potential and providing certainty in the accuracy. Method also promises to remove all the mentioned anomalies from the system like lack of accuracy and automated approach in pre- processing of the data set. In result analysis, it has been observed that prediction is much more efficient and minimum accuracy achieved through this proposed method is 87.64% on any of the data set taken under consideration.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]mu.edu.sg

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