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).
|