Applying Hunger Game Search (HGS) for selecting significant blood indicators for early prediction of ICU COVID-19 severity

• Autorzy: Sayed Safynaz AbdEl-Fattah , ElKorany Abeer , Sayed Sabah

Identyfikatory

DOI

10.7494/csci.2023.24.1.4654

• Języki publikacji: EN

Abstrakty

EN

This paper introduces an early prognostic model for attempting to predict the severity of patients for ICU admission and detect the most significant features that affect the prediction process using clinical blood data. The proposed model predicts ICU admission for high-severity patients during the first two hours of hospital admission, which would help assist clinicians in decision-making and enable the efficient use of hospital resources. The Hunger Game search (HGS) meta-heuristic algorithm and a support vector machine (SVM) have been integrated to build the proposed prediction model. Furthermore, these have been used for selecting the most informative features from blood test data. Experiments have shown that using HGS for selecting features with the SVM classifier achieved excellent results as compared with four other meta-heuristic algorithms. The model that used the features that were selected by the HGS algorithm accomplished the topmost results (98.6 and 96.5%) for the best and mean accuracy, respectively, as compared to using all of the features that were selected by other popular optimization algorithms.

Słowa kluczowe

EN

ICU severity prediction; COVID-19; clinical blood tests; Hunger Game search; HGS; optimization algorithm; support vector machine; SVM; feature selection

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2023
• Tom: T. 24 (1)
• Strony: 113--136
• Opis fizyczny: Bibliogr. 72 poz., rys., tab.

Twórcy

autor

Sayed Safynaz AbdEl-Fattah

• Beni-Suef University, Faculty of Computers and Artificial Intelligence, 62521, Egypt

autor

ElKorany Abeer

• Cairo University, Faculty of Computers and Artificial Intelligence, 12613, Egypt

autor

Sayed Sabah

• Cairo University, Faculty of Computers and Artificial Intelligence, 12613, Egypt

Bibliografia

• [1] Aktar S., Ahamad M.M., Rashed-Al-Mahfuz M., Azad A.K.M., Uddin S., Kamal A.H.M., Alyami S.A., et al.: Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development, JMIR Medical Informatics, vol. 9(4), 2021.
• [2] Alakus T.B., Turkoglu I.: Comparison of deep learning approaches to predict COVID-19 infection, Chaos, Solitons & Fractals, vol. 140, 2020.
• [3] AlJame M., Ahmad I., Imtiaz A., Mohammed A.: Ensemble learning model for diagnosing COVID-19 from routine blood tests, Informatics in Medicine Unlocked, vol. 21, 2020.
• [4] Aljameel S.S., Khan I.U., Aslam N., Aljabri M., Alsulmi E.S.: Machine LearningBased Model to Predict the Disease Severity and Outcome in COVID-19 Patients, Scientific Programming, vol. 2021, 2021.
• [5] Alotaibi A., Shiblee M., Alshahrani A.: Prediction of severity of COVID-19- infected patients using machine learning techniques, Computers, vol. 10(3), 2021.
• [6] An C., Lim H., Kim D.W., Chang J.H., Choi Y.J., Kim S.W.: Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study, Scientific Reports, vol. 10(1), pp. 1–11, 2020.
• [7] Arevalo-Rodriguez I., Buitrago-Garcia D., Simancas-Racines D., ZambranoAchig P., Del Campo R., Ciapponi A., Sued O., Martinez-Garcia L., Rutjes A.W., Low N., et al.: False-negative results of initial RT-PCR assays for COVID-19: a systematic review, PloS one, vol. 15(12), p. e0242958, 2020.
• [8] Aschenbrenner A.C., Mouktaroudi M., Krämer B., Oestreich M., Antonakos N., Nuesch-Germano M., Gkizeli K., et al.: Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients, Genome Medicine, vol. 13(1), pp. 1–25, 2021.
• [9] Bajaj V., Gadi N., Spihlman A.P., Wu S.C., Choi C.H., Moulton V.R.: Aging, immunity, and COVID-19: how age influences the host immune response to coronavirus infections?, Frontiers in Physiology, vol. 11, 2021.
• [10] Bajgain K.T., Badal S., Bajgain B.B., Santana M.J.: Prevalence of comorbidities among individuals with COVID-19: A rapid review of current literature, American Journal of Infection Control, vol. 49(2), pp. 238–246, 2021.
• [11] Ben-Hur A., Weston J.: A user’s guide to support vector machines. In: Data mining techniques for the life sciences, pp. 223–239, Springer, 2010.
• [12] Bertsimas D., Lukin G., Mingardi L., Nohadani O., Orfanoudaki A., Stellato B., Wiberg H., et al.: COVID-19 mortality risk assessment: An international multicenter study, PloS one, vol. 15(12), 2020.
• [13] Bravata D.M., Perkins A.J., Myers L.J., Arling G., Zhang Y., Zillich A.J., Reese L., et al.: Association of intensive care unit patient load and demand with mortality rates in US Department of Veterans Affairs hospitals during the COVID-19 pandemic, JAMA Network Open, vol. 4(1), 2021.
• [14] Cascella M., Rajnik M., Aleem A., Dulebohn S.C., Di Napoli R.: Features, evaluation, and treatment of coronavirus (COVID-19), StatPearls, 2021.
• [15] Cascella M., Rajnik M., Aleem A., Dulebohn S.C., Di Napoli R.: Features, evaluation, and treatment of coronavirus (COVID-19), StatPearls, 2022.
• [16] Cheng F.Y., Joshi H., Tandon P., Freeman R., Reich D.L., Mazumdar M., KohliSeth R., et al.: Using machine learning to predict ICU transfer in hospitalized COVID-19 patients, Journal of Clinical Medicine, vol. 9(6), 2020.
• [17] Cheng Y., Luo R., Wang K., Zhang M., Wang Z., Dong L., Li J., et al.: Kidney disease is associated with in-hospital death of patients with COVID-19, Kidney International, vol. 97(5), pp. 829–838, 2020.
• [18] Chung A., Famouri M., Hryniowski A., Wong A.: COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification, arXiv preprint arXiv:210906711, 2021.
• [19] Corinna C., Vapnik V.: Support-Vector Networks, Machine Learning, vol. 20, pp. 273–297, 1995. doi: 10.1023/A:1022627411411.
• [20] Corman V.M., Landt O., Kaiser M., Molenkamp R., Meijer A., Chu D.K., Bleicker T., et al.: Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR, Eurosurveillance, vol. 25(3), 2020.
• [21] Cover T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition, IEEE Transactions on Electronic Computers, vol. EC-14(3), pp. 326–334, 1965. doi: 10.1109/PGEC.1965.264137.
• [22] Deif M.A., Solyman A.A., Alsharif M.H., Uthansakul P.: Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach, Sensors, vol. 21(19), 2021.
• [23] EL-Hasnony I.M., Elhoseny M., Tarek Z.: A hybrid feature selection model based on butterfly optimization algorithm: COVID-19 as a case study, Expert Systems, 2021. doi: 10.1111/exsy.12786.
• [24] Elezagic D., Johannis W., Burst V., Klein F., Streichert T.: Venous blood gas analysis in patients with COVID-19 symptoms in the early assessment of virus positivity, Journal of Laboratory Medicine, vol. 45(1), pp. 27–30, 2021.
• [25] de Fátima Cobre A., Stremel D.P., Noleto G.R., Fachi M.M., Surek M., Wiens A., Tonin F.S., Pontarolo R.: Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?, Computers in Biology and Medicine, 2021.
• [26] de Freitas Barbosa V.A., Gomes J.C., de Santana M.A., Albuquerque de J.E., de Souza R.G., de Souza R.E., dos Santos W.P.: Heg. IA: an intelligent system to support diagnosis of Covid-19 based on blood tests, Research on Biomedical Engineering, vol. 38, pp. 99–116, 2022.
• [27] Friedrichs F., Igel C.: Evolutionary tuning of multiple SVM parameters, Neurocomputing, vol. 64, pp. 107–117, 2005.
• [28] Ghaemi M., Feizi-Derakhshi M.R.: Feature selection using forest optimization algorithm, Pattern Recognition, vol. 60, pp. 121–129, 2016.
• [29] Guhathakurata S., Kundu S., Chakraborty A., Banerjee J.S.: A novel approach to predict COVID-19 using support vector machine. In: Data Science for COVID-19, pp. 351–364, Elsevier, 2021.
• [30] Henry B.M., Aggarwal G., Wong J., Benoit S., Vikse J., Plebani M., Lippi G.: Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: a pooled analysis, The American Journal of Emergency Medicine, vol. 38(9), pp. 1722–1726, 2020.
• [31] Hospital Sírio-Libanês: COVID-19 – Clinical Data to assess diagnosis, 2020. https://www.kaggle.com/S%C3%ADrio-Libanes/covid19.
• [32] Jiang S.Q., Huang Q.F., Xie W.M., Lv C., Quan X.Q.: The association between severe COVID-19 and low platelet count: evidence from 31 observational studies involving 7613 participants, British Journal of Haematology, vol. 190, pp. e29–e33, 2020.
• [33] Jiang X., Coffee M., Bari A., Wang J., Jiang X., Huang J., Shi J., et al.: Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity, Computers, Materials & Continua, vol. 63(1), pp. 537–551, 2020.
• [34] Karimi Shahri M., Niazkar H.R., Rad F.: COVID-19 and hematology findings based on the current evidences: a puzzle with many missing pieces, International Journal of Laboratory Hematology, vol. 43(2), pp. 160–168, 2021.
• [35] Karthikeyan A., Garg A., Vinod P., Priyakumar U.D.: Machine learning based clinical decision support system for early COVID-19 mortality prediction, Frontiers in Public Health, vol. 9, 2021.
• [36] Kishaba T., Tamaki H., Shimaoka Y., Fukuyama H., Yamashiro S.: Staging of acute exacerbation in patients with idiopathic pulmonary fibrosis, Lung, vol. 192(1), pp. 141–149, 2014.
• [37] Kojadinovic I., Wottka T.: Comparison between a filter and a wrapper approach to variable subset selection in regression problems. In: Proceedings European Symposium on Intelligent Techniques (ESIT), pp. 311–321, ESIT, Aachen, Germany, 2000.
• [38] Lameski P., Zdravevski E., Mingov R., Kulakov A.: SVM parameter tuning with grid search and its impact on reduction of model over-fitting. In: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. 15th International Conference, RSFDGrC 2015, Tianjin, China, November 20–23, 2015, Proceedings,, pp. 464–474, Springer, 2015.
• [39] Li X., Ge P., Zhu J., Li H., Graham J., Singer A., Richman P.S., Duong T.Q.: Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables, PeerJ, vol. 8, 2020.
• [40] Li Z., Yi Y., Luo X., Xiong N., Liu Y., Li S., Sun R., et al.: Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-2 infection diagnosis, Journal of Medical Virology, vol. 92(9), pp. 1518–1524, 2020.
• [41] Lippi G., South A.M., Henry B.M.: Electrolyte imbalances in patients with severe coronavirus disease 2019 (COVID-19), Annals of Clinical Biochemistry, vol. 57(3), pp. 262–265, 2020.
• [42] Liu Y., Mao B., Liang S., Yang J.W., Lu H.W., Chai Y.H., Wang L., et al.: Association between age and clinical characteristics and outcomes of COVID-19, European Respiratory Journal, vol. 55(5), 2020. doi: 10.1183/13993003.01112- 2020.
• [43] Lundberg S.M., Lee S.I.: A Unified Approach to Interpreting Model Predictions. In: NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017. https: //proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767- Paper.pdf.
• [44] de Moraes Batista A.F., Miraglia J.L., Rizzi Donato T.H., Porto Chiavegatto Filho A.D.: COVID-19 diagnosis prediction in emergency care patients: a machine learning approach, medRxiv, 2020. doi: 10.1101/2020.04.04.20052092.
• [45] Nabil E., Sayed S.A.F., Hameed H.A.: An efficient binary clonal selection algorithm with optimum path forest for feature selection, International Journal of Advanced Computer Science and Applications, vol. 11(7), 2020.
• [46] Nakeshbandi M., Maini R., Daniel P., Rosengarten S., Parmar P., Wilson C., Kim J.M., et al.: The impact of obesity on COVID-19 complications: a retrospective cohort study, International Journal of Obesity, vol. 44(9), pp. 1832–1837, 2020.
• [47] Onan A., Korukoğlu S.: A feature selection model based on genetic rank aggregation for text sentiment classification, Journal of Information Science, vol. 43(1), pp. 25–38, 2017.
• [48] Pan A., Liu L., Wang C., Guo H., Hao X., Wang Q., Huang J., et al.: Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China, Jama, vol. 323(19), pp. 1915–1923, 2020.
• [49] Peer N., Lombard C., Steyn K., Levitt N.: Elevated resting heart rate is associated with several cardiovascular disease risk factors in urban-dwelling black South Africans, Scientific Reports, vol. 10(1), 2020.
• [50] Phienthrakul T., Kijsirikul B.: Evolutionary strategies for multi-scale radial basis function kernels in support vector machines. In: GECCO’05: Proceedings of the 7th annual conference on Genetic and evolutionary computation, pp. 905–911, 2005.
• [51] Ran J., Song Y., Zhuang Z., Han L., Zhao S., Cao P., Geng Y., et al.: Blood pressure control and adverse outcomes of COVID-19 infection in patients with concomitant hypertension in Wuhan, China, Hypertension Research, vol. 43(11), pp. 1267–1276, 2020.
• [52] Sayed S.A.F., Elkorany A.M., Mohammad S.S.: Applying different machine learning techniques for prediction of COVID-19 severity, IEEE Access, vol. 9, pp. 135697–135707, 2021.
• [53] Sayed S.A.F., Nabil E., Badr A.: A binary clonal flower pollination algorithm for feature selection, Pattern Recognition Letters, vol. 77, pp. 21–27, 2016.
• [54] Schwab P., Schütte A., Dietz B., Bauer S.: predCOVID-19: A systematic study of clinical predictive models for coronavirus disease 2019, arXiv preprint arXiv:200508302, vol. 76, 2020.
• [55] Sen-Crowe B., Sutherland M., McKenney M., Elkbuli A.: A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic, Journal of Surgical Research, vol. 260, pp. 56–63, 2021.
• [56] Shang Y., Pan C., Yang X., Zhong M., Shang X., Wu Z., Yu Z., et al.: Management of critically ill patients with COVID-19 in ICU: statement from front-line intensive care experts in Wuhan, China, Annals of Intensive Care, vol. 10(1), 2020.
• [57] Soui M., Mansouri N., Alhamad R., Kessentini M., Ghedira K.: NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms, Nonlinear Dynamics, vol. 106, pp. 1453–1475, 2021.
• [58] Syarif I., Prugel-Bennett A., Wills G.: SVM parameter optimization using grid search and genetic algorithm to improve classification performance, TELKOMNIKA Telecommunication Computing Electronics and Control, vol. 14(4), pp. 1502–1509, 2016.
• [59] Tan L., Wang Q., Zhang D., Ding J., Huang Q., Tang Y.Q., Wang Q., Miao H.: Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study, Signal Transduction and Targeted Therapy, vol. 5(1), 2020.
• [60] Uhl B., Vadlau Y., Zuchtriegel G., Nekolla K., Sharaf K., Gaertner F., Massberg S., Krombach F., Reichel C.A.: Aged neutrophils contribute to the first line of defense in the acute inflammatory response, Blood, vol. 128(19), pp. 2327–2337, 2016.
• [61] Wang K., Zuo P., Liu Y., Zhang M., Zhao X., Xie S., Zhang H., Chen X., Liu C.: Clinical and laboratory predictors of in-hospital mortality in patients with COVID-19: a cohort study in Wuhan, China, Clinical Infectious Diseases, vol. 71(16), pp. 2079–2088, 2020.
• [62] Wilcoxon F.: Individual comparisons by ranking methods. In: Breakthroughs in statistics. Volume II. Methodology and Distribution, pp. 196–202, Springer, 1992.
• [63] World Health Organization: Clinical management of COVID-19: interim guidance, 27 May 2020. Technical report, 2020.
• [64] World Health Organization: Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases: interim guidance, 2 March 2020. Technical report, 2020.
• [65] Wu J., Zhang P., Zhang L., Meng W., Li J., Tong C., Li Y., et al.: Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results, MedRxiv, 2020.
• [66] Xu W., Sun N.N., Gao H.N., Chen Z.Y., Yang Y., Ju B., Tang L.L.: Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning, Scientific Reports, vol. 11(1), 2021.
• [67] Yan L., Zhang H.T., Goncalves J., Xiao Y., Wang M., Guo Y., Sun C., et al.: An interpretable mortality prediction model for COVID-19 patients, Nature Machine Intelligence, vol. 2(5), pp. 283–288, 2020.
• [68] Yang Y., Chen H., Heidari A.A., Gandomi A.H.: Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts, Expert Systems with Applications, vol. 177, 2021. doi: 10.1016/ j.eswa.2021.114864.
• [69] Yao H., Zhang N., Zhang R., Duan M., Xie T., Pan J., Peng E., et al.: Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests, Frontiers in Cell and Developmental Biology, vol. 8, 2020.
• [70] Yasin R., Gouda W.: Chest X-ray findings monitoring COVID-19 disease course and severity, Egyptian Journal of Radiology and Nuclear Medicine, vol. 51(1), 2020.
• [71] Yu L., Halalau A., Dalal B., Abbas A.E., Ivascu F., Amin M., Nair G.B.: Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19, PloS One, vol. 16(4), 2021.
• [72] Zhu N., Zhang D., Wang W., Li X., Yang B., Song J., Zhao X., et al.: A novel coronavirus from patients with pneumonia in China, 2019, New England Journal of Medicine, vol. 382(8), pp. 727–733, 2020.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).