Document Type : Original/Review Paper


1 Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran

2 Center for optimization and intelligent decision making in healthcare systems (COID-Health), Isfahan University of Technology, Isfahan, Iran.


In recent years, the occurrence of various pandemics (COVID-19, SARS, etc.) and their widespread impact on human life have led researchers to focus on their pathology and epidemiology components. One of the most significant inconveniences of these epidemics is the human mortality rate, which has highly social adverse effects. This study, in addition to major attributes affecting the COVID-19 mortality rate (Health factors, people-health status, and climate) considers the social and economic components of societies. These components have been extracted from the countries’ Human Development Index (HDI) and the effect of the level of social development on the mortality rate has been investigated using ensemble data mining methods. The results indicate that the level of community education has the highest effect on the disease mortality rate. In a way, the extent of its effect is much higher than environmental factors such as air temperature, regional health factors, and community welfare. This factor is probably due to the ability of knowledge-based societies to manage the crises, their attention to health advisories, lower involvement of rumors, and consequently lower incidence of mental health problems. This study shows the impact of education on reducing the severity of the crisis in communities and opens a new window in terms of cultural and social factors in the interpretation of medical data. Furthermore, according to the results and comparing different types of single and ensemble data mining methods, the application of the ensemble method in terms of classification accuracy and prediction error has the best result.


Main Subjects

[1]           World Health Organization. "" (accessed.
[2]           I. Gilles et al., "Trust in medical organizations predicts pandemic (H1N1) 2009 vaccination behavior and perceived efficacy of protection measures in the Swiss public," European Journal of Epidemiology, vol. 26, pp. 203-210, 2011, DOI: 10.1007/s10654-011-9577-2.
[3]           W. van der Weerd, D. R. Timmermans, D. J. Beaujean, J. Oudhoff, and J. E. Van Steenbergen, "Monitoring the level of government trust, risk perception and intention of the general public to adopt protective measures during the influenza A (H1N1) pandemic in The Netherlands," BMC Public Health, vol. 11, p. 575, 2011, DOI: 10.1186/1471-2458-11-575.
[4]           A. Takian, M. M. Kiani, and K. Khanjankhani, "COVID-19 and the need to prioritize health equity and social determinants of health," International Journal of Public Health, vol. in the press, 2020.
[5]           A. Zaveri and P. Chouhan, "Are child and youth population at lower risk of COVID-19 fatalities? Evidence from South-East Asian and European countries," Children and Youth Services Review, vol. 119, p. 105360, 2020.
[6]           S. W.-. Betech, C. G. Cassandras, and I. C. Paschalidis, "Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator," International Journal of Medical Informatics, vol. 142, p. 104258, 2020.
[7]           R. Harris, "Exploring the neighborhood-level correlates of Covid-19 deaths in London using a difference across spatial boundaries method," Health & Place, vol. 66, p. 102446, 2020.
[8]           F. J. Elgar, A. Stefaniak, and M. J. A. Wohl, "The trouble with trust: Time-series analysis of social capital, income inequality, and COVID-19 deaths in 84 countries," Social Science & Medicine, vol. 263, p. 113365, 2020.
[9]           S. Kaur, P. Kaul, and P. MoradianZadeh, "Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data," Procedia Computer Science, vol. 177, pp. 423–430, 2020.
[10]         A. M. Figueiredo, A. Daponte-Codina, Daniela, C. M. Marculino, P. V. Rodrigo, d. L. Kenio Costa, and G.-G. Eugenia, "Factores asociados a la incidencia y la mortalidad por COVID-19 en las comunidades autónomasFactors associated with the incidence and mortality from COVID-19 in the autonomous communities of Spain," Gaceta Sanitaria, vol. In Press, 2020.
[11]         S. Hamidi, R. Ewing, and S. Sabouri, "Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States," Health & Place, vol. 64, p. 102378, 2020.
[12]         R. B. Hawkins, E. J. Charles, and J. H. Mehaffey, "Socio-economic status and COVID-19–related cases and fatalities," Public Health, vol. 189, pp. 29-134, 2020.
[13]         J.-D. Zeitoun, M. Faron, and J. H. Lefèvre, "Impact of the local care environment and social characteristics on aggregated hospital fatality rate from COVID-19 in France: a nationwide observational study," Public Health, vol. 189, pp. 104-109, 2020.
[14]         G. J. Bamber, S. Ryan, and N. Wailes, "Globalization, employment relations, and human resources indicators in ten developed market economies: international data sets," The International Journal of Human Resource Management, vol. 15, no. 8, pp. 1481-1516, 2004, DOI:
[15]         J. Susnik and P. v. d. Zaang, "Correlation and causation between the UN Human Development Index and national and personal wealth and resource exploitation," Economic Research-Ekonomska Istraživanja, vol. 30, no. 1, pp. 1705-1723, 2017, DOI: 10.1080/1331677X.2017.1383175.
[16]         World Bank Group. "Climate Change Knowledge Portal." (accessed.
[17]         United Nations Development Programme. "" (accessed.
[18]         O. Oladimeji and O. Oladimeji, "Detecting Breast Cancer through Blood Analysis Data using Classification Algorithms," Journal of AI & Data Mining, vol. 9, no. 3, pp. 359-351, 2021.
[19]         M. Salehi, J. Razmara, and Sh. Lotfi, "Development of an Ensemble Multi-stage Machine for Prediction of Breast Cancer Survivability," Journal of AI & Data Mining, vol. 8, no. 3, pp. 378-371, 2020.
[20]         H. Azan et al., "IoMT amid COVID-19 pandemic: Application, architecture, technology, and security," Journal of Network and Computer Applications, p. 102886, 2020.
[21]         B. Tesfaye, S. Atique, and N. Elias, "Determinants and development of a web-based child mortality prediction model in resource-limited settings: A data mining approach," Computer Methods and Programs in Biomedicine vol. 140, pp. 45-51, 2017.
[22]         M. Eliot, L. Azzoni, and C. Firnhaber, "Tree-Based Methods for Discovery of Association between Flow Cytometry Data and Clinical Endpoints," Adv Bioinformatics, 2009.
[23]         P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, and C. Shearer, CRISP-DM 1.0: Step-by-step data mining guide. 2000.
[24]         Dataset Publishing Language. "Dataset Publishing Language, countries.csv." (accessed.
[25]         Young MoonChae, Seung HeeHo, Kyoung WonCho, Dong HaLee, and Sun HaJi, "Data mining approach to policy analysis in a health insurance domain," International Journal of Medical Informatics, vol. 62, no. 2-3, pp. 103-111
[26]         B. Samwaysdos Santos, M. Teresinh ArnsSteiner, A. TrojanFenerich, A. Henrique, and P. Lima, "Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018," Computers & Industrial Engineering, vol. 138, p. 106120, 2019.
[27]         B. Robson and S. Boray, "Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data," Computers in Biology and Medicine, vol. 112, p. 103369, 2019.
[28]         T. Käkilehto, S. kaSalo, and M. Larmas, "Data mining of clinical oral health documents for analysis of the longevity of different restorative materials in Finland," International Journal of Medical Informatics, vol. 78, no. 12, pp. e68-e74, 209.
[29]         K. Eyasu, W. Jimma, and T. Tadesse, "Developing a Prototype Knowledge-Based System for Diagnosis and Treatment of Diabetes Using Data Mining Techniques," Ethiopian Journal of health sciences, vol. 30, no. 1, pp. 115-124, 2020, DOI:
[30]         L. Schroeder, M. R. Veronez, E. M. de Souza, D. Brum, L. Gonzaga, and V. F. Rofatto, "Respiratory diseases, malaria, and leishmaniasis: Temporal and spatial association with fire occurrences from knowledge discovery and data mining," International Journal of Environmental Research and Public Health, vol. 17, no. 10, 2020.
[31]         C. Neto, M. Brito, V. Lopes, H. Peixoto, A. Abelha, and J. Machado, "Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients," Entropy, vol. 21, no. 12, 2019.
[32]         W. Meng, Ou, W., S. Chandwani, X. Chen, W. Black, and Z. Cai, "Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer," Journal of Biomedical Informatics, vol. 100, 2019, DOI: 10.1016/j.jbi.2019.103335.
[33]         H. Rawashdeh et al., "Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage, " Computational Biology and Chemistry, 2020, DOI:
[34]         T. R. Stella Mary and S. Sebastian, "Predicting heart ailment in patients with a varying number of features using data mining techniques," International Journal of Electrical and Computer Engineering, vol. 9, no. 4, pp. 2675-2681, 2019.
[35]         T. Srivastava, A. Bhatnagar, J. Jayapradha, and M. Prakash, "Diabetes detection and monitoring using data mining and machine learning," International Journal of Advanced Science and Technology, vol. 29, pp. 1889-1897, 2020.
[36]         A. S. Albahri, R. A. Hamid, J. Alwan, and Z. T. Al-qays, "Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review," Journal of Medical Systems, vol. 44, no. 7, 2020.
[37]         T. Mohana Priya and M. Punithavalli, "An efficient data mining techniques - Multi-objective KNN algorithm to predict breast cancer," International Journal of Recent Technology and Engineering, vol. 8, pp. 986-990, 2019.
[38]         J. Diz, G. Marreiros, and A. Freitas, "Applying Data Mining Techniques to Improve Breast Cancer Diagnosis," Journal of Medical Systems, vol. 40, no. 9, 2016.
[39]         M. Manjusree and K. A. Sateesh Kumar, "Diabetes prediction using data mining classification techniques," International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 5901-5905, 2019.
[40]         V. Vapnik, S. Golowich, and A. Smola, "Support vector method for function approximation, regression estimation and signal processing," Advances in neural information processing systems, vol. 9, pp. 281-287, 1996.
[41]         T. Fusco, Bi, Y., H. Wang, and F. Browne, "approach for prediction modeling of schistosomiasis disease vectors: Epidemic disease prediction modeling," International Journal of Machine Learning and Cybernetics, vol. 11, no. 6, pp. 1159-1178, 2020.
[42]         S. Geeitha and M. Thangamani, "A cognizant study of machine learning in predicting cervical cancer at various levels-a data mining concept," International Journal on Emerging Technologies, vol. 11, no. 1, pp. 23-28, 2020.
[43]         H. Ayatollahi, L. Gholamhosseini, and M. Salehi, "Predicting coronary artery disease: A comparison between two data mining algorithms," BMC Public Health, vol. 19, no. 1, 2019, DOI: 10.1186/s12889-019-6721-5.
[44]         A. Dela Cruz Galapon, "An assessment: Respiratory analysis using data mining method - A decision support system," Test Engineering and Management, vol. 83, pp. 4824-4829, 2020.
[45]         Yulong. Bai, Lihong. Tang, Manhong. Fan, Xiaoyan. Ma, and Y. Yang, "Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts," Energies, vol. 13, no. 3332, 2020, DOI: 10.3390/en13133332.
[46]         S. Simsek, U. Kursuncu, E. Kibis, M. AnisAbdellatif, and A. Dag, "A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival," Expert Systems with Applications, vol. 139, 2020.
[47]         Li-Hong. Tang, Yu-Long. Bai, Jie. Yang, and Y.-N. Lu, "A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series," Chaos, Solitons, and Fractals, vol. 141, no. 110366, 2020, DOI: