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http://dx.doi.org/10.22937/IJCSNS.2021.21.6.12

Heart Attack Prediction using Neural Network and Different Online Learning Methods  

Antar, Rayana Khaled (Umm Al-Qura University, Department of Computer Science)
ALotaibi, Shouq Talal (Umm Al-Qura University, Department of Computer Science)
AlGhamdi, Manal (Umm Al-Qura University, Department of Computer Science)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.6, 2021 , pp. 77-88 More about this Journal
Abstract
Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.
Keywords
Neural Network; Heart Attack; online learning; Optimization; Regularization;
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1 Samarasinghe, S.. Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Crc Press; 2016.
2 Matic, V., et al. Effective diagnosis of heart disease presence using artificial neural networks. In: Sinteza 2017-International Scientific Conference on Information Technology and Data Related Research. Singidunum University; 2017, p. 3-8.8.
3 Brownlee, J..Train-test split for evaluating machine learning algorithms. Available at https://machinelearningmastery.com/train-test-split-forevaluating-machine-learning-algorithms/ (2020/08/26).
4 Harris, C.R., Millman, K.J., van der Walt, S.J., Gommers, R., Virtanen, P., Cournapeau, D., et al. Array programming with NumPy. Nature 2020;585(7825):357-362. doi: \bibinfo{doi}{10.1038/s41586-020-2649-2}. URL https://doi.org/10.1038/s41586-020-2649-2.   DOI
5 Peng, C.C., Huang, C.W., Lai, Y.C.. Heart disease prediction using artificial neural networks: A survey. In:2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE; 2020, p. 147-150.
6 Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., et al. Api design for machine learning software: experiences from the scikit-learn project. ArXiv preprint arXiv :13090238 2013.
7 Chollet, F., et al. Keras. https://github.com/fchollet/keras; 2015.
8 Brownlee, J.. A gentle introduction to dropout for regularizing deep neural networks. Available at https://machinelearningmastery.com/dropout-forregularizing-deep-neural-networks/ (2019/08/06).
9 Olaniyi, E.O., Oyedotun, O.K., Adnan, K.. Heart diseases diagnosis using neural networks arbitration. International Journal of Intelligent Systems and Applications 2015;7(12):72.
10 Haq, A.U., Li, J.P., Memon, M.H., Nazir, S., Sun, R.. A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Information Systems2018;2018.
11 Durairaj, M., Revathi, V. Prediction of heart disease using back propagation mlp algorithm. International Journal of Scientific & Technology Research 2015;4(8):235-239.
12 Awan, S.E., Bennamoun, M., Sohel, F., Sanfilippo, F.M., Dwivedi, G.. Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics. ESC heart failure 2019;6(2):428-435.   DOI
13 Takci, H.. Improvement of heart attack prediction by the feature selection methods. Turkish Journal of Electrical Engineering & Computer Sciences 2018;26(1):1-10.   DOI
14 Xiao, T., Zhang, L., Ma, S.. System Simulation and Scientific Computing: International Conference, ICSC 2012, Shanghai, China, October27-30, 2012. Proceedings, Part I; vol. 326. Springer; 2012.20.
15 Yosi Taguri, S.E., Lussato, R.. 7 types of neural network activation functions: How to choose? Available at https://missinglink.ai/guides/neural-network-concepts/7-types-neural-network-activation-functions-right/ (2016).
16 Chandra, K., Meijer, E., Andow, S., Arroyo-Fang, E., Dea, I., George, J., et al. Gradient descent: The ultimate optimizer. arXiv preprintarXiv:190913371 2019.
17 Mahapatra, A.. Momentum in machine learning by medium. Available at https://medium.com (2019/06/14).
18 Cheng, C.A., Chiu, H.W.. An artificial neural network model for the evaluation of carotid artery stenting prognosis using a national-wide database. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2017, p.2566-2569.14.
19 AlindGupta, g.. Regularization in machine learning. Available at https://www.geeksforgeeks.org/regularizationin-machine-learning/amp/ (2020/08/21).
20 Le, H.M., Tran, T.D., Van Tran, L.. Automatic heart disease prediction using feature selection and data mining technique. Journal of Computer Science and Cybernetics 2018;34(1):33-48.   DOI
21 Inc., K.. Heart disease uci. 2019.
22 Malav, A., Kadam, K., Kamat, P.. Prediction of heart disease using k-means and artificial neural network as hybrid approach to improve accuracy. International Journal of Engineering and Technology2017;9(4):3081-3085.   DOI
23 Panday, P., Godara, N.. Decision support system for cardiovascular heart disease diagnosis using improved multilayer perceptron. International Journal of Computer Applications 2012;45(8).12.
24 Hegde, S., Hedge, R.. Symmetry based feature selection with multilayer perceptron for the prediction of chronic disease. International Journal of Recent Technology and Engineering 2019;8(2):3316-3322.10.   DOI
25 Adnan, J., Daud, N.N., Ahmad, S., Mat, M., Ishak, M., Hashim, F., et al. Heart abnormality activity detection using multilayer perceptron (mlp) network. In: AIP Conference Proceedings; vol. 2016. AIP Publishing LLC; 2018, p. 020013.15.
26 geeksforgeeks.org. Ml -stochastic gradient descent (sgd). Available at https://www.geeksforgeeks.org/ml-stochasticgradient-descent-sgd/ (2020/05/16).
27 Farahmand, A.m.. Regularization in reinforcement learning 2011.
28 Gupta,P..Cross-validation in machine learning. Available at https://towardsdatascience.com/cross-validation-inmachine-learning-72924a69872f (2020/08/21).
29 Kluyver, T., Ragan-Kelley, B., Perez, F., Granger, B., Bussonnier, M., Frederic, J., et al. Jupyter notebooks - a publishing format for reproducible computational workflows. In: Loizides, F., Schmidt, B., editors. Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press; 2016, p. 87 - 90.26.
30 pandas development team, T.. pandas-dev/pandas : Pandas. 2020. doi:\bibinfo{doi}{10.5281/zenodo.3509134}. URL https://doi.org/10.5281/zenodo.3509134.28.
31 Aakash Nain Sayak Paul, M.M.R.. Keras. Available at https://keras.io/about// (2020/08/26).