An Effective Heart Disease Prediction Model Using Deep Learning-based Dimensionality Reduction on Imbalanced Data

Kabiri Rad, S.; Afshin, V.; Zahiri, S. H.

doi:10.22061/jecei.2024.10847.742

Articles in Press

Document Type : Original Research Paper

Authors

¹ Department of Computer Science, Birjand University of Technology, Birjand, Iran.

² Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

https://doi.org/10.22061/jecei.2024.10847.742

Abstract

Background and Objectives: When dealing with high-volume and high-dimensional datasets, the distribution of samples becomes sparse, and issues such as feature redundancy or irrelevance arise. Dimensionality reduction techniques aim to incorporate correlation between features and map the original features into a lower dimensional space. This usually reduces the computational burden and increases performance. In this paper, we study the problem of predicting heart disease in a situation where the dataset is large and (or) the proportion of instances belonging to one class compared to others is significantly low.
Methods: We investigated three of the prominent dimensionality reduction techniques, including Principal Component Analysis (PCA), Information Bottleneck (IB) and Variational Autoencoder (VAE) on popular classification algorithms. To have adequate samples in all classes to properly feed the classifier, an efficient data balancing technique is used to compensate for fewer positives than negatives. Among all data balancing methods, a SMOTE-based method is selected, which generates new samples at the boundary of the samples distribution and avoids the synthesis of noise and redundant data.
Results: The experimental results show that VAE-based method outperforms other dimensionality reduction algorithms in the performance measures. The proposed hybrid method improves accuracy to 97.1% and sensitivity to 99.2%.
Conclusion: Finally, it can be concluded that the combination of VAE with oversampling algorithms can significantly enhance system performance as well as computational time.

Keywords

Main Subjects

Artificial Intelligence

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

Publisher’s Note

JECEI Publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher

Shahid Rajaee Teacher Training University

References

[1] U. Kose et al., "A practical method for early diagnosis of heart diseases via deep neural network," in Deep Learning for Medical Decision Support Systems, pp. 95-106, 2021.

[2] A. A. Ali, H. S. Hassan, E. M. Anwar, A. Khanna, "Hybrid technique for heart diseases diagnosis based on convolution neural network and long short-term memory," in Applications of Big Data in Healthcare: Elsevier, pp. 261-280, 2021.

[3] D. Pratella, S. Ait-El-Mkadem Saadi, S. Bannwarth, V. Paquis-Fluckinger, S. Bottini, "A survey of autoencoder algorithms to pave the diagnosis of rare diseases," Int. J. Mol. Sci., 22(19): 10891, 2021.

[4] N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," J. Artif. Intell. Res., 16: 321-357, 2002.

[5] H. Han, W. Y. Wang, B. H. Mao, "Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning," in Proc. International Conference on Intelligent Computing: 878-887, 2005.

[6] C. M. Bhatt, P. Patel, T. Ghetia, P. L. Mazzeo, "Effective heart disease prediction using machine learning techniques," Algorithms, 16(2): 88, 2023.

[7] A. Khan, M. Qureshi, M. Daniyal, K. Tawiah, "A novel study on machine learning algorithm‐based cardiovascular disease prediction," Health Social Care Community, 2023(1): 1406060, 2023.

[8] D. Hassan, H. I. Hussein, M. M. Hassan, "Heart disease prediction based on pre-trained deep neural networks combined with principal component analysis," Biomed. Signal Process. Control, 79: 104019, 2023.

[9] S. Kabirirada, H. Kardanmoghaddamb, V. Afshin, "Heart disease prediction by using artificial neural networks," Int. J. Comput. Sci. Inf. Secur., 14(1), 2016.

[10] A. Ahmed, S. A. Hannan, "Data mining techniques to find out heart diseases: an overview," Int. J. Innovative Technol. Exploring Eng. (IJITEE), 1(4): 18-23, 2012.

[11] S. I. Ayon, M. M. Islam, M. R. Hossain, "Coronary artery heart disease prediction: A comparative study of computational intelligence techniques," IETE J. Res., 68(4): 2488-2507, 2022.

[12] K. Li, A. Zhu, W. Zhou, P. Zhao, J. Song, J. Liu, "Utilizing deep learning to optimize software development processes," arXiv preprint arXiv:2404.13630, 2024.

[13] S. Shilaskar , A. Ghatol, "Feature selection for medical diagnosis: Evaluation for cardiovascular diseases," Expert Syst. Appl., 40(10): 4146-4153, 2013.

[14] Y. E. Shao, C. D. Hou, C. C. Chiu, "Hybrid intelligent modeling schemes for heart disease classification," Appl. Soft Comput., 14: 47-52, 2014.

[15] R. R. Ema, P. C. Shill, "Integration of fuzzy C-Means and artificial neural network with principle component analysis for heart disease prediction," in Proc. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT): 1-6, 2020.

[16] I. D. Mienye, Y. Sun, Z. Wang, "Improved sparse autoencoder based artificial neural network approach for prediction of heart disease," Inf. Med. Unlocked, 18: 100307, 2020.

[17] T. Amarbayasgalan, V. H. Pham, N. Theera-Umpon, Y. Piao, K. H. Ryu, "An efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets," IEEE Access, 9: 135210-135223, 2021.

[18] S. A. Ebiaredoh-Mienye, E. Esenogho, T. G. Swart, "Integrating enhanced sparse autoencoder-based artificial neural network technique and softmax regression for medical diagnosis," Electronics, 9(11): 1963, 2020.

[19] S. M. S. Shah, S. Batool, I. Khan, M. U. Ashraf, S. H. Abbas, S. A. Hussain, "Feature extraction through parallel probabilistic principal component analysis for heart disease diagnosis," Physica A, 482: 796-807, 2017.

[20] T. Zhang, B. Yang, "Big data dimension reduction using PCA," in Proc. 2016 IEEE International Conference on Smart Cloud (SmartCloud): 152-157, 2016.

[21] F. Anowar, S. Sadaoui, B. Selim, "Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne)," Comput. Sci. Rev., 40: 100378, 2021.

[22] S. Zhang, "Nearest neighbor selection for iteratively kNN imputation," J. Syst. Software, 85(11): 2541-2552, 2012.

[23] ”Kaggle Cardiovascular Disease Dataset," Accessed 1 November 2022.

[24] ”UCI Machine Learning Repository. Uci.edu.," Accessed 14 June 2022.

[25] A. Kolchinsky, B. D. Tracey, D. H. Wolpert, "Nonlinear information bottleneck," Entropy, 21(12): 1181, 2019.

[26] S. M. Saqlain et al., "Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines," Knowl. Inf. Syst., 58: 139-167, 2019.

[27] S. Mohan, C. Thirumalai, G. Srivastava, "Effective heart disease prediction using hybrid machine learning techniques," IEEE Access, 7: 81542-81554, 2019.

[28] A. CS, S. Lal, V. PRABHU GURUPUR, P. P. Saxena, "Multi-modal medical image fusion with adaptive weighted combination of NSST bands using chaotic grey wolf optimization," IEEE Access, 7: 40782-40796, 2019.

[29] N. L. Fitriyani, M. Syafrudin, G. Alfian, J. Rhee, "HDPM: an effective heart disease prediction model for a clinical decision support system," IEEE Access, 8: 133034-133050, 2020.

[30] R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, P. Singh, "Prediction of heart disease using a combination of machine learning and deep learning," Comput. Intell. Neurosci., 2021(1): 8387680, 2021.

[31] M. M. Hossain et al., "Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI," Inf. Med. Unlocked, 42: 101370, 2023.

[32] G. Manikandan, B. Pragadeesh, V. Manojkumar, A. Karthikeyan, R. Manikandan, A. H. Gandomi, "Classification models combined with Boruta feature selection for heart disease prediction," Inf. Med. Unlocked, 44: 101442, 2024.

LETTERS TO EDITOR

Journal of Electrical and Computer Engineering Innovations (JECEI) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in JECEI should be sent to the editorial office of JECEI within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Journal of Electrical and Computer Engineering Innovations (JECEI)

An Effective Heart Disease Prediction Model Using Deep Learning-based Dimensionality Reduction on Imbalanced Data

References

References

Send comment about this article

Articles in Press, Accepted Manuscript
Available Online from 21 October 2024

An Effective Heart Disease Prediction Model Using Deep Learning-based Dimensionality Reduction on Imbalanced Data

References

References

Send comment about this article

Articles in Press, Accepted Manuscript Available Online from 21 October 2024

Articles in Press, Accepted Manuscript
Available Online from 21 October 2024