Feature Reinforcement using Autoencoders
Cardiovascular disease (CVD) is the number one cause of death globally, more people die annually from CVDs than from any other cause. People with cardiovascular disease or who are at high cardiovascular risk need early detection and management using counselling and medicines, as appropriate. The early detection of CVDs needs an expert hand and awareness amongst people. Here is where Data analytics can help in predicting the cardiovascular cases before-hand by helping to make informed decisions faster, with great accuracy and at a much earlier date. The dataset used is the Cleveland Heart disease Database taken from UCI learning data set repository. The dataset is being divided into five classes, 0 corresponding to absence of any disease and 1,2,3,4 corresponding to grades of heart disease. The dataset has been bifurcated into absence (0) and presence (1, 2, 3 and 4) of the heart disease. Using medical profiles such as age, sex, blood pressure, cholesterol, sugar level etc. The classifiers can predict the probability of patients getting a heart disease. There is no dearth of classification techniques but feature engineering and data representation is the crux of the model building pre-activity. When done efficiently, this could make the model more robust and accurate. We are introducing an idea of feature reinforcement technique using Artificial Neural Networks (MLP)-Auto encoders. In this technique we would represent the features in an abstracted format using MLPAutoencoders and then reinforce the input features with the abstracted features. This activity would exhaustively capture the latency in input features thus making our feature representation more robust and resilient. We have tested our technique on Cleveland Heart disease dataset. The results obtained by using our technique had higher degree of accuracy than the results obtained with input features alone.
 [Hinton and Salakhutdinov, 2006] Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786):504507.
 Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning Book
 Random Foreset –Random Features Leo Breiman Statistics Department University of California Berkeley, CA 94720 Technical Report 567
 Alain, G. and Bengio, Y. (2012). What regularized auto-encoders learn from the data generating distribution. Technical Report Arxiv report 1211.4246, Universite de Montreal.
 Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y. (2011a). Contractive auto-encoders: Explicit invariance during feature extraction. In ICML2011.
 Weston, J., Ratle, F., and Collobert, R. (2008). Deep learning via semisupervised embedding. In ICML 2008.
 Representation Learning: A Review and New Perspectives. Yoshua Bengio, Aaron Courville, and Pascal Vincent Department of computer science and operations research, U. Montreal also, Canadian Institute for Advanced Research (CIFAR) 13-15
 Hinton, G., Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. Science, 313, 504507.
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :