Entropy pelican optimization algorithm (epoa) based feature selection and deep autoencoder (dae) of heart failure status prediction
DOI:
https://doi.org/10.56294/sctconf2024761Keywords:
Heart Failure (HF), Dilated Cardiomyopathy (DCM), Entropy Pelican Optimization Algorithm (EPOA), Gene Expression Omnibus (GEO), Deep Autoencoder (DAE), FS (Feature Selection)Abstract
Introduction: heart Failure (HF) is a complicated condition as well as a significant public health issue. Data processing is now required for machine and statistical learning techniques while it helps to identify key features and eliminates unimportant, redundant, or noisy characteristics, hence minimizing the feature space's dimensions. A common cause of mortality in cases of heart disease is Dilated Cardiomyopathy (DCM).
Methods: the feature selection in this work depends on the Entropy Pelican Optimization Algorithm (EPOA). It is a recreation of pelicans' typical hunting behaviour. This is comparable to certain characteristics that lead to better approaches for solving high-dimensional datasets. Then Deep Autoencoder (DAE) classifier has been introduced for the prediction of patients. DAE classifier is employed to compute the system's nonlinear function through data from the normal and failure state.
Results: DAE was discovered to not only considerably increase accuracy but also to be beneficial when there is a limited amount of labelled data.Performance metrics like recall, precision, accuracy, f-measure, and error rate has been used for results analysis.
Conclusion: publicly available benchmark dataset has been collected from Gene Expression Omnibus (GEO) repository to evaluate and contrast the suitability of the suggested classifier with other existing methods
References
1. Bozkurt B, Colvin M, Cook J, Cooper LT, Deswal A, Fonarow GC, Francis GS, Lenihan D, Lewis EF, McNamara DM, and Pahl E. Current diagnostic and treatment strategies for specific dilated cardiomyopathies: a scientific statement from the American Heart Association. Circulation, 134(23), pp. e579-e646. https://doi.org/10.1161/CIR.0000000000000455.
2. Pinto YM, Elliott PM, Arbustini E, Adler Y, Anastasakis A, Böhm M, Duboc D, Gimeno J, de Groote P, Imazio M, and Heymans S. Proposal for a revised definition of dilated cardiomyopathy, hypokinetic non-dilated cardiomyopathy, and its implications for clinical practice: a position statement of the ESC working group on myocardial and pericardial diseases. European heart journal, 37(23), pp. 1850-1858. https://doi.org/10.1093/eurheartj/ehv727.
3. Merlo M, Cannatà A, Pio Loco C, Stolfo D, Barbati G, Artico J, Gentile P, De Paris V, Ramani F, Zecchin M, and Gigli M. Contemporary survival trends and aetiological characterization in non‐ischaemic dilated cardiomyopathy. European Journal of Heart Failure, 22(7), pp. 1111-1121. https://doi.org/10.1002/ejhf.1914.
4. Miladinović A, Ajčević M, Jarmolowska J, Marusic U, Colussi M, Silveri G, Battaglini PP, and Accardo A. Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study. Computer Methods and Programs in Biomedicine, 198, pp. 105808. https://doi.org/10.1016/j.cmpb.2020.105808.
5. Obermeyer Z, and Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine, 375(13), pp. 1216–1219. https://doi.org/10.1056%2FNEJMp1606181.
6. Elshawi R, Al-Mallah MH, and Sakr S. On the interpretability of machine learning-based model for predicting hypertension. BMC medical informatics and decision making, 19, pp. 1-32. https://doi.org/10.1186/s12911-019-0874-0.
7. Krittanawong C, Zhang H, Wang Z, Aydar M, and Kitai T. Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), pp. 2657-2664. https://doi.org/10.1016/j.jacc.2017.03.571.
8. Li Y, Mansmann U, Du S, and Hornung R. Benchmark study of feature selection strategies for multi-omics data. BMC bioinformatics, 23(1), pp. 1-18. https://doi.org/10.1186/s12859-022-04962-x.
9. Yan C, Li M, Ma J, Liao Y, Luo H, Wang J, and Luo J. A novel feature selection method based on MRMR and enhanced flower pollination algorithm for high dimensional biomedical data. Current Bioinformatics, 17(2), pp. 133-149. https://doi.org/10.2174/1574893616666210624130124.
10. Rahkar Farshi T. Battle royale optimization algorithm. Neural Computing and Applications, 33(4), pp. 1139-1157. https://doi.org/10.1007/s00521-020-05004-4.
11. Schiano C, Franzese M, Geraci F, Zanfardino M, Maiello C, Palmieri V, Soricelli A, Grimaldi V, Coscioni E, Salvatore M, and Napoli C. Machine learning and bioinformatics framework integration to potential familial DCM-related markers discovery. Genes, 12(12), pp.1-14. https://doi.org/10.3390/genes12121946.
12. Zhu T, Wang M, Quan J, Du Z, Li Q, Xie Y, Lin M, Xu C, and Xie Y. Identification and verification of feature biomarkers associated with immune cells in dilated cardiomyopathy by bioinformatics analysis. Frontiers in Genetics, 13, pp. 1-13. https://doi.org/10.3389/fgene.2022.874544.
13. Chen K, Shi Y, and Zhu H. Analysis of the role of glucose metabolism-related genes in dilated cardiomyopathy based on bioinformatics. Journal of Thoracic Disease, 15(7), pp. 3870-3884. https://doi.org/10.21037%2Fjtd-23-906.
14. Xie W, Li W, Zhang S, Wang L, Yang J, and Zhao D. A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data. BMC bioinformatics, 23(1), pp. 1-18. https://doi.org/10.1186/s12859-022-04848-y.
15. Kang Y, Wang H, Pu B, Tao L, Chen J, and Philip SY. A hybrid two-stage teaching-learning-based optimization algorithm for feature selection in bioinformatics. IEEE/ACM transactions on computational biology and bioinformatics, pp. 1746-1760. https://doi.org/10.1109/TCBB.2022.3215129.
16. Yang, Y., Liu, P., Teng, R., Liu, F., Zhang, C., Lu, X. and Ding, Y., 2022. Integrative bioinformatics analysis of potential therapeutic targets and immune infiltration characteristics in dilated cardiomyopathy. Annals of Translational Medicine, 10(6), pp. 1-16. https://doi.org/10.21037%2Fatm-22-732.
17. Jiang C, and Zhong G. Identify the characteristic genes of dilated cardiomyopathy and immune cell infiltration based on bioinformatics and machine learning. Xi bao yu fen zi Mian yi xue za zhi= Chinese Journal of Cellular and Molecular Immunology, 39(1), pp. 26-33.
18. Dauda KA, Olorede KO, and Aderoju SA. A novel hybrid dimension reduction technique for efficient selection of bio-marker genes and prediction of heart failure status of patients. Scientific African, 12, pp. e00778. https://doi.org/10.1016/j.sciaf.2021.e00778.
19. Chen YX, Ding J, Zhou WE, Zhang X, Sun XT, Wang XY, Zhang C, Li N, Shao GF, Hu SJ, and Yang J. Identification and functional prediction of long non-coding RNAs in dilated cardiomyopathy by bioinformatics analysis. Frontiers in Genetics, 12, pp. 1-12. https://doi.org/10.3389/fgene.2021.648111.
20. Song W, Lu F, Ding Z, Huang L, Hu K, Chen J, and Wei L. Identification of heparan sulfate in dilated cardiomyopathy by integrated bioinformatics analysis. Frontiers in Cardiovascular Medicine, 9, pp. 1-10. https://doi.org/10.3389/fcvm.2022.900428.
21. Zhang M, Wang X, Chen W, Liu W, Xin J, Yang D, Zhang Z, and Zheng X. Integrated bioinformatics analysis for identifying key genes and pathways in female and male patients with dilated cardiomyopathy. Scientific Reports, 13(1), pp. 1-12. https://doi.org/10.1038/s41598-023-36117-0.
22. Rau A, Marot G, and Jaffrézic F. Differential meta-analysis of RNA-seq data from multiple studies. BMC bioinformatics, 15, pp.1-10. https://doi.org/10.1186/1471-2105-15-91.
23. Trojovský P, and Dehghani M. Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22(3), pp.1-34. https://doi.org/10.3390/s22030855.
24. Li H, Meng L, Zhang J, Tan Y, Ren Y, and Zhang H. Multiple description coding based on convolutional auto-encoder. Ieee Access, 7, pp. 26013-26021. https://doi.org/10.1109/ACCESS.2019.2900498.
25. Pan Y, He F, and Yu H. Learning social representations with deep autoencoder for recommender system. World Wide Web, 23(4), pp. 2259-2279. https://doi.org/10.1007/s11280-020-00793-z.
26. Yu J, Zheng X, and Wang S. A deep autoencoder feature learning method for process pattern recognition. Journal of Process Control, 79, pp. 1-15. https://doi.org/10.1016/j.jprocont.2019.05.002.
27. Toma RN, Piltan F, and Kim JM. A deep autoencoder-based convolution neural network framework for bearing fault classification in induction motors. Sensors, 21(24), pp. 1-21. https://doi.org/10.3390/s21248453.
Published
Issue
Section
License
Copyright (c) 2024 Ms. T. Sangeetha, Dr. K. Manikandan, Dr. D. Victor Arokia Doss (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.