Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases
Downloads
Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases.
Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy.
Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training.
Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%).
Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin
Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization
A. J. Fasunla, M. Samdi, and O. G. Nwaorgu, "An audit of ear, nose and throat diseases in a tertiary health institution in South-Western Nigeria,” Pan Afr. Med. J., vol. 14, 2013, doi: 10.11604/pamj.2013.14.1.1092.
L. Lukama, C. Aldous, C. Michelo, and C. Kalinda, "Ear, Nose and Throat (ENT) disease diagnostic error in low-resource health care: Observations from a hospital-based cross-sectional study,” PLoS One, vol. 18, no. 2 February, 2023, doi: 10.1371/journal.pone.0281686.
R. M. Krol et al., "Immunosuppressive Therapies in Ear, Nose, and Throat Involvement in Antineutrophil Cytoplasmic Antibody-Associated Vasculitis: Results From a Multicenter Retrospective Cohort Study,” J. Rheumatol., vol. 50, no. 3, 2023, doi: 10.3899/jrheum.220343.
S. S. Chandrasekhar et al., "Clinical Practice Guideline: Sudden Hearing Loss (Update),” Otolaryngol. - Head Neck Surg. (United States), vol. 161, no. 1_suppl, 2019, doi: 10.1177/0194599819859885.
L. K. Dillard, R. X. Martinez, L. L. Perez, A. M. Fullerton, S. Chadha, and C. M. McMahon, "Prevalence of aminoglycoside-induced hearing loss in drug-resistant tuberculosis patients: A systematic review,” Journal of Infection, vol. 83, no. 1. 2021. doi: 10.1016/j.jinf.2021.05.010.
E. Widyawati and S. Sudaryanti, "Responsivitas Dinas Kesehatan Kabupaten Sragen dalam Upaya Mengurangi Angka Stunting pada Balita,” Wacana Publik, vol. 2, no. 1, 2022, doi: 10.20961/wp.v2i1.63281.
V. Vaks et al., "Novel Approaches in the Diagnostics of Ear-Nose-Throat Diseases Using High-Resolution THz Spectroscopy,” Appl. Sci., vol. 13, no. 3, 2023, doi: 10.3390/app13031573.
D. Novaliendry, C. H. Yang, and A. Y. Denno Guara Labukti, "The expert system application for diagnosing human vitamin deficiency through forward chaining method,” in International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015, 2015. doi: 10.1109/ICTC.2015.7354493.
B. H. Hayadi et al., "Expert system of quail disease diagnosis using forward chaining method,” Indones. J. Electr. Eng. Comput. Sci., vol. 5, no. 1, 2017, doi: 10.11591/ijeecs.v5.i1.pp206-213.
A. A. Perbawawati, E. Sugiharti, and M. A. Muslim, "Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs,” Sci. J. Informatics, vol. 6, no. 1, 2019, doi: 10.15294/sji.v6i1.14149.
E. Pawan, R. M. . Thamrin, W. Widodo, S. H. Y. B. Sariaty H.Y.Bei, and J. J. Luanmasa, "Implementation of Forward Chaining Method in Expert System to Detect Diseases in Corn Plants in Muara Tami District,” Int. J. Comput. Inf. Syst., vol. 3, no. 1, 2022, doi: 10.29040/ijcis.v3i1.59.
C. Hayat and B. Abian, "The modeling of artificial neural network of early diagnosis for malnutrition with backpropagation method,” in Proceedings of the 3rd International Conference on Informatics and Computing, ICIC 2018, 2018. doi: 10.1109/IAC.2018.8780505.
C. Hayat, S. Limong, and N. Sagala, "Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders,” Khazanah Inform. J. Ilmu Komput. dan Inform., 2019, doi: 10.23917/khif.v5i2.7923.
B. Wang, Q. Chen, Z. Wang, and Y. Hu, "The Rearch on Improved LVQ Neural Network Method,” in 2019 3rd International Conference on Circuits, System and Simulation, ICCSS 2019, 2019. doi: 10.1109/CIRSYSSIM.2019.8935608.
K. Kumar and A. Abhishek, "Artificial Neural Networks for Diagnosis of Kidney Stones Disease,” Int. J. Inf. Technol. Comput. Sci., vol. 4, no. 7, 2012, doi: 10.5815/ijitcs.2012.07.03.
M. Latifi and H. Latifi, "A Combined Approach for Text Detection in Images Using MLP Neural Networks and Image Processing,” in Advances in Intelligent Systems and Computing, 2021. doi: 10.1007/978-3-030-73103-8_54.
B Ster and A Dobnikar, "Neural Networks in Medical Diagnosis: Comparison with Other Methods,” Proc. Int. Conf. EANN96, vol. 1, no. September 2015, 1996.
H. T. Gorji and J. Haddadnia, "A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI,” Neuroscience, vol. 305, 2015, doi: 10.1016/j.neuroscience.2015.08.013.
G. J. J. de Vries, S. C. Pauws, and M. Biehl, "Insightful stress detection from physiology modalities using Learning Vector Quantization,” Neurocomputing, vol. 151, no. P2, 2015, doi: 10.1016/j.neucom.2014.10.008.
U. Asan, "Competitive positioning analysis with learning vector quantization,” in Advances in Intelligent Systems and Computing, 2020. doi: 10.1007/978-3-030-23756-1_46.
R. Ghaffari et al., "Early detection of diseases in tomato crops: An electronic nose and intelligent systems approach,” in Proceedings of the International Joint Conference on Neural Networks, 2010. doi: 10.1109/IJCNN.2010.5596535.
P. L. Rocha and W. L. S. Silva, "Intelligent system of speech recognition using Neural Networks based on DCT parametric models of low order,” in Proceedings of the International Joint Conference on Neural Networks, 2016. doi: 10.1109/IJCNN.2016.7727280.
Z. Yan, H. Du, and Z. Xu, "Face detection and description based on video structural description technologies,” in Advances in Intelligent Systems and Computing, 2018. doi: 10.1007/978-3-319-67071-3_2.
C. S. Kanimozhiselvi and A. Pratap, "Possibilistic LVQ neural network - An application to childhood autism grading,” Neural Netw. World, vol. 26, no. 3, 2016, doi: 10.14311/NNW.2016.26.014.
D. Whitley, "A genetic algorithm tutorial,” Stat. Comput., vol. 4, no. 2, 1994, doi: 10.1007/BF00175354.
A. Maskooki, K. Deb, and M. Kallio, "A customized genetic algorithm for bi-objective routing in a dynamic network,” Eur. J. Oper. Res., vol. 297, no. 2, 2022, doi: 10.1016/j.ejor.2021.05.018.
B. Chowdhury and G. Garai, "A review on multiple sequence alignment from the perspective of genetic algorithm,” Genomics, vol. 109, no. 5–6. 2017. doi: 10.1016/j.ygeno.2017.06.007.
M. Kumar, M. Husain, N. Upreti, and D. Gupta, "Genetic Algorithm: Review and Application,” SSRN Electron. J., 2020, doi: 10.2139/ssrn.3529843.
V. Biberacher et al., "Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis,” Neuroimage, vol. 142, 2016, doi: 10.1016/j.neuroimage.2016.07.035.
J. M. Wang and Y. Q. Wen, "Application of genetic LVQ neural network in credit analysis of power customer,” in Proceedings - 4th International Conference on Natural Computation, ICNC 2008, 2008. doi: 10.1109/ICNC.2008.158.
Y. Zhang and M. Li, "An evaluation model of water quality based on Learning Vector Quantization neural network,” in Chinese Control Conference, CCC, 2016. doi: 10.1109/ChiCC.2016.7553926.
R. Vipani, S. Hore, S. Basu, S. Basak, and S. Dutta, "Identification of epileptic seizures using Hilbert transform and learning vector quantization based classifier,” in 2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings, 2018. doi: 10.1109/CALCON.2017.8280702.
J. G. Monroe, J. Ducoste, and E. Z. Berglund, "Genetic Algorithm–Genetic Programming Approach to Identify Hierarchical Models for Ultraviolet Disinfection Reactors,” J. Environ. Eng., vol. 145, no. 2, 2019, doi: 10.1061/(asce)ee.1943-7870.0001492.
R. I. Woodward and E. J. R. Kelleher, "Towards ‘smart lasers': Self-optimisation of an ultrafast pulse source using a genetic algorithm,” Sci. Rep., vol. 6, 2016, doi: 10.1038/srep37616.
A. G. Karegowda, A. . Manjunath, and M. . Jayaram, "Application of Genetic Algorithm Optimized Neural Network Connection Weights for Medical Diagnosis of PIMA Indians Diabetes,” Int. J. Soft Comput., vol. 2, no. 2, 2011, doi: 10.5121/ijsc.2011.2202.
P. Melin, V. Herrera, D. Romero, F. Valdez, and O. Castillo, "Genetic optimization of neural networks for person recognition based on the Iris,” Telkomnika, vol. 10, no. 2, 2012, doi: 10.12928/telkomnika.v10i2.800.
S. Katoch, S. S. Chauhan, and V. Kumar, "A review on genetic algorithm: past, present, and future,” Multimed. Tools Appl., vol. 80, no. 5, 2021, doi: 10.1007/s11042-020-10139-6.
U. G. Inyang, E. E. Akpan, and O. C. Akinyokun, "A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification,” Int. J. Comput. Intell. Appl., 2020, doi: 10.1142/S1469026820500121.
E. Ahlqvist et al., "Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables,” Lancet Diabetes Endocrinol., 2018, doi: 10.1016/S2213-8587(18)30051-2.
N. Ghaffar Nia, E. Kaplanoglu, and A. Nasab, "Evaluation of artificial intelligence techniques in disease diagnosis and prediction,” Discov. Artif. Intell., vol. 3, no. 1, 2023, doi: 10.1007/s44163-023-00049-5.
F. Zhang et al., "Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.2999608.
J. McCall, "Genetic algorithms for modelling and optimisation,” J. Comput. Appl. Math., vol. 184, no. 1, 2005, doi: 10.1016/j.cam.2004.07.034.
A. Alharbi, "A Genetic-LVQ neural networks approach for handwritten Arabic character recognition,” Artif. Intell. Res., vol. 7, no. 2, 2018, doi: 10.5430/air.v7n2p43.
G. Nagarajan, R. I. Minu, B. Muthukumar, V. Vedanarayanan, and S. D. Sundarsingh, "Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection,” in Procedia Computer Science, 2016. doi: 10.1016/j.procs.2016.05.192.
M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and S. Shahab, "Deep learning for stock market prediction,” Entropy, vol. 22, no. 8, 2020, doi: 10.3390/E22080840.
A. MG, "Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis,” J. Med. Diagnostic Methods, vol. 05, no. 01, 2016, doi: 10.4172/2168-9784.1000205.
A. Muñoz and F. Rubio, "Evaluating genetic algorithms through the approximability hierarchy,” J. Comput. Sci., vol. 53, 2021, doi: 10.1016/j.jocs.2021.101388.
B. Dirgantara and H. Hairani, "Sistem Pakar Diagnosa Penyakit THT Menggunakan Inferensi Forward Chaining dan Metode Certainty Factor,” J. Bumigora Inf. Technol., vol. 3, no. 1, 2021, doi: 10.30812/bite.v3i1.1241.
Copyright (c) 2024 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
