Improved Heart Disease Prediction Using Deep Neural Network
DOI:
https://doi.org/10.51983/ajcst-2019.8.2.2141Keywords:
Deep Neural Network, Heart Attack, Machine LearningAbstract
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.
References
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.
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.
Yuming Hua, Junhai Guo, and Hua Zhao, "Deep Belief Networks and deep learning," Proc. 2015 Int. Conf. Intell. Comput. Internet Things, pp. 1–4.
J. Schmidhuber, "Deep Learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117, Jan 2015.
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.
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.
S. Hochreiter and J. UrgenSchmidhuber, "Long Short-Term Memory," Neural Compuational., vol. 9, no. 8, pp. 1735–1780, 1997.
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.
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.
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. [Online] Available: http://www.ieee.org/
D. K. Srivastava and L. Bhambhu, "Data classification using support vector machine," J. Theor. Appl. Inf. Technol., pp. 1-6, 2009.
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.
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 The Research Publication
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
