Abstract
Identifying Chronic Obstructive Pulmonary Disease (COPD) is essential for reducing mortality and cost burden. However, the population suffers from an underdiagnosis of chronic obstructive pulmonary disease. This chapter aims to create COPD detection models and assess the relative effectiveness of several modeling paradigms to discover the optimal model for the task on the dataset of 563 hospital or emergency ward visits in China-Japan Friendship Hospital performed between February 2011 and March 2017. We investigated the use of a Long Short Term Memory Network (LSTM), a kind of deep learning, for the automated identification of COPD, with the model hyperparameters modified using the firefly algorithm. Three optimization variations have been used to optimize the hyperparameters of the proposed LSTM Model: random search, hyperband, and firefly algorithm. Firefly algorithm with LSTM obtained superior results than the LSTM-Random Search and LSTM-Hyperband. Therefore, the adoption of LSTM-Firefly is beneficial in terms of COPD detection and diagnosis with clinically acceptable performance compared to LSTM—Random Search, LSTM—Hyperband, LSTM, and other machine learning algorithms such as LR, KNN, NB, DT, and RF.
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References
Iadanza, E., Mudura, V., Melillo, P., Gherardelli, M.: An automatic system supporting clinical decision for chronic obstructive pulmonary disease. Health Technol. (Berl) 10(2), 487–498 (2020). https://doi.org/10.1007/s12553-019-00312-9
Haider, N.S., Singh, B.K., Periyasamy, R., Behera, A.K.: Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. J. Med. Syst. 43(8), 255 (2019). https://doi.org/10.1007/s10916-019-1388-0
Wang, C., Chen, X., Du, L., Zhan, Q., Yang, T., Fang, Z.: Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease. Comput. Methods Programs Biomed. 188, 105267 (2020). https://doi.org/10.1016/j.cmpb.2019.105267
Fister, I., Yang, X.-S., Fister, I., Brest, J.: Memetic firefly algorithm for combinatorial optimization. Proc. 5th Int. Conf. Bioinspired Optim. Methods their Appl. BIOMA 2012, pp. 75–86 (2012), [Online]. Available: http://arxiv.org/abs/1204.5165
Amaral, J.L.M., Lopes, A.J., Faria, A.C.D., Melo, P.L.: Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease. Comput. Methods Programs Biomed. 118(2), 186–197 (2015). https://doi.org/10.1016/j.cmpb.2014.11.002
Tang, L.Y.W., Coxson, H.O., Lam, S., Leipsic, J., Tam, R.C., Sin, D.D.: Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT. Lancet Digit. Heal. 2(5), e259–e267 (2020). https://doi.org/10.1016/S2589-7500(20)30064-9
Zheng, H., et al.: Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine. J. Clin. Lab. Anal. 35(2), 1–8 (2021). https://doi.org/10.1002/jcla.23641
Dhar, J.: Multistage ensemble learning model with weighted voting and genetic algorithm optimization strategy for detecting chronic obstructive pulmonary disease. IEEE Access 9, 48640–48657 (2021). https://doi.org/10.1109/ACCESS.2021.3067949
Vicente, J.M.F., Álvarez-Sánchez, J.R., De La Paz López, F., Toledo-Moreo, F.J., Adeli, H.: Artificial Computation in Biology and Medicine: International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2015 Elche, Spain, June 1–5, 2015 Proceedings, Part I. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9107, 305–311 (2015). doi:https://doi.org/10.1007/978-3-319-18914-7
Ma, X., et al.: Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population. J. Transl. Med. 18(1), 1–14 (2020). https://doi.org/10.1186/s12967-020-02312-0
Goto, T., Jo, T., Matsui, H., Fushimi, K., Hayashi, H., Yasunaga, H.: Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease. COPD J. Chronic Obstr. Pulm. Dis. 16(5–6), 338–343 (2019). https://doi.org/10.1080/15412555.2019.1688278
Das Joshe, M., Emon, N.H., Islam, M., Ria, N.J., Masum, A.K.M., Noori, S.R.H.: Symptoms Analysis Based Chronic Obstructive Pulmonary Disease Prediction In Bangladesh Using Machine Learning Approach, no. December, pp. 1–5 (2021). doi:https://doi.org/10.1109/icccnt51525.2021.9580078
Zafari, H., Langlois, S., Zulkernine, F., Kosowan, L., Singer, A.: Predicting chronic obstructive pulmonary disease from EMR data. 2020 IEEE Conf. Comput. Intell. Bioinforma. Comput. Biol. CIBCB 2020 (2020). doi:https://doi.org/10.1109/CIBCB48159.2020.9277712
Suresh Kumar, P., Behera, H.S., Nayak, J., Naik, B.: Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature. Innov. Syst. Softw. Eng. (September 2019), 1–22 (2021). doi:https://doi.org/10.1007/s11334-021-00399-2
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Kumar, P.S., Dash, P.B., Rao, B.K., Vimal, S., Muhammad, K. (2023). Early Detection of Chronic Obstructive Pulmonary Disease Using LSTM-Firefly Based Deep Learning Model. In: Nayak, J., Das, A.K., Naik, B., Meher, S.K., Brahnam, S. (eds) Nature-Inspired Optimization Methodologies in Biomedical and Healthcare. Intelligent Systems Reference Library, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-031-17544-2_11
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