Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches

Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches
  • World Health Organization. Children: Reducing Mortality (World Health Organization, 2019).

    Google Scholar 

  • Rudan, I. et al. Global estimate of the incidence of clinical pneumonia among children under five years of age. Bull. World Health Organ. 82(12), 895–903 (2004).

    PubMed 

    Google Scholar 

  • Goodarzi, E. et al. Epidemiology of mortality induced by acute respiratory infections in infants and children under the age of 5 years and its relationship with the Human Development Index in Asia: An updated ecological study. J. Public Health 29(5), 1047–1054 (2021).

    Article 

    Google Scholar 

  • Organization, W. H. World Report on Ageing and Health (World Health Organization, 2015).

    Google Scholar 

  • Anjum, M. U., Riaz, H. & Tayyab, H. M. Acute respiratory tract infections (Aris);: Clinico-epidemiolocal profile in children of less than five years of age. Prof. Med. J. 24(02), 322–325 (2017).

    Google Scholar 

  • Ujunwa, F. & Ezeonu, C. Risk factors for acute respiratory tract infections in under-five children in enugu Southeast Nigeria. Ann. Med. Health Sci. Res. 4(1), 95–99 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sultana, M. et al. Prevalence, determinants and health care-seeking behavior of childhood acute respiratory tract infections in Bangladesh. PloS one 14(1), e0210433 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kjærgaard, J. et al. Diagnosis and treatment of acute respiratory illness in children under five in primary care in low-, middle-, and high-income countries: A descriptive FRESH AIR study. PLoS One 14(11), e0221389 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Banda, B. et al. Risk factors associated with acute respiratory infections among under-five children admitted to Arthur’s Children Hospital, Ndola, Zambia. Asian Pac. J. Health Sci. 3(3), 153–159 (2016).

    Article 

    Google Scholar 

  • Harerimana, J.-M. et al. Social, economic and environmental risk factors for acute lower respiratory infections among children under five years of age in Rwanda. Arch. Public Health 74(1), 1–7 (2016).

    Article 

    Google Scholar 

  • Landrigan, P. J. et al. The Lancet Commission on pollution and health. Lancet 391(10119), 462–512 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Lelieveld, J. et al. Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovasc. Res. 116(11), 1910–1917 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mirabelli, M. C., Ebelt, S. & Damon, S. A. Air quality index and air quality awareness among adults in the United States. Environ. Res. 183, 109185 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fleming, S. et al. Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: A systematic review of observational studies. Lancet 377(9770), 1011–1018 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gasana, J. et al. Motor vehicle air pollution and asthma in children: A meta-analysis. Environ. Res. 117, 36–45 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Osborne, S. et al. Air quality around schools: Part II-mapping PM2.5 concentrations and inequality analysis. Environ. Res. 197, 111038 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Vong, C.-M. et al. Imbalanced learning for air pollution by meta-cognitive online sequential extreme learning machine. Cognit. Comput. 7, 381–391 (2015).

    Article 

    Google Scholar 

  • Ginantra, N., Indradewi, I. & Hartono E. Machine learning approach for acute respiratory infections (ISPA) prediction: Case study indonesia. in Journal of Physics: Conference series. (IOP Publishing, 2020).

  • Ku, Y. et al. Machine learning models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. Clin. Exp. Otorhinolaryngol. 15(2), 168 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ravindra, K. et al. Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections. Sci. Total Environ. 858, 159509 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Aliaga, A. & Ren, R. The Optimal Sample Sizes for Two-Stage Cluster Sampling in Demographic and Health Surveys (ORC Macro, 2006).

    Google Scholar 

  • Hammer, M. S. et al. Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ. Sci. Technol. 54(13), 7879–7890 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Croft, T. N. et al. Guide to DHS Statistics Vol. 645 (Rockville, ICF, 2018).

    Google Scholar 

  • Organization, W.H., Global influenza strategy 2019–2030. (2019).

  • Kjærgaard, J. et al. Correction: Diagnosis and treatment of acute respiratory illness in children under five in primary care in low-, middle-, and high-income countries: A descriptive FRESH AIR study. Plos one 15(2), e0229680 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fetene, M. T., Fenta, H. M. & Tesfaw, L. M. Spatial heterogeneities in acute lower respiratory infections prevalence and determinants across Ethiopian administrative zones. J. Big Data 9(1), 1–16 (2022).

    Article 

    Google Scholar 

  • Yu, H.-F., Huang, F.-L. & Lin, C.-J. Dual coordinate descent methods for logistic regression and maximum entropy models. Mach. Learn. 85(1–2), 41–75 (2011).

    Article 
    MathSciNet 

    Google Scholar 

  • Arthur, E. H. & Robert, W. K. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970).

    Article 

    Google Scholar 

  • Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996).

    Article 
    MathSciNet 

    Google Scholar 

  • Zou, H. & Hastie, T. Addendum: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67(5), 768–768 (2005).

    Article 
    MathSciNet 

    Google Scholar 

  • Géron, A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (O’Reilly Media, 2019).

    Google Scholar 

  • James, G. et al. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).

    Book 

    Google Scholar 

  • Patrick, E. A. & Fischer, F. P. III. A generalized k-nearest neighbor rule. Inform. Control 16(2), 128–152 (1970).

    Article 
    MathSciNet 

    Google Scholar 

  • McCallum, A. & Nigam K. A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization. (Madison, 1998).

  • Zhang, D. Bayesian classification. In Fundamentals of Image Data Mining 161–178 (Springer, 2019).

    Chapter 

    Google Scholar 

  • Chen, T. & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2016), KDD ‘16, ACM. (2016).

  • Chen, T. & Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (2016).

  • Hecht-Nielsen, R. Theory of the backpropagation neural network. In Neural networks for perception 65–93 (Elsevier, 1992).

    Chapter 

    Google Scholar 

  • Abdelhafiz, D. et al. Deep convolutional neural networks for mammography: Advances, challenges and applications. BMC Bioinform. 20(11), 1–20 (2019).

    Google Scholar 

  • Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970).

    Article 

    Google Scholar 

  • Molina, M. & Garip, F. Machine learning for sociology. Ann. Rev. Sociol. 45, 27–45 (2019).

    Article 

    Google Scholar 

  • Marsland, S. Machine Learning: An Algorithmic Perspective (CRC Press, 2015).

    Google Scholar 

  • Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301–320 (2005).

    Article 
    MathSciNet 

    Google Scholar 

  • Yuan, G.-X., Ho, C.-H. & Lin, C.-J. An improved glmnet for l1-regularized logistic regression. J. Mach. Learn. Res. 13(1), 1999–2030 (2012).

    MathSciNet 

    Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45(1), 5–32 (2001).

    Article 

    Google Scholar 

  • Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. Variable selection using random forests. Pattern Recognit. Lett. 31(14), 2225–2236 (2010).

    Article 
    ADS 

    Google Scholar 

  • Janitza, S., Tutz, G. & Boulesteix, A.-L. Random forest for ordinal responses: Prediction and variable selection. Comput. Stat. Data Anal. 96, 57–73 (2016).

    Article 
    MathSciNet 

    Google Scholar 

  • Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. VSURF: An R package for variable selection using random forests. R J. 7(2), 19–33 (2015).

    Article 

    Google Scholar 

  • Pal, M. Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26(1), 217–222 (2005).

    Article 

    Google Scholar 

  • Rodriguez-Galiano, V. F. et al. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 67, 93–104 (2012).

    Article 
    ADS 

    Google Scholar 

  • Liaw, A. & Wiener, M. Classification and regression by randomForest. R news 2(3), 18–22 (2002).

    Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article 

    Google Scholar 

  • Quinlau, R. Induction of decision trees. Mach. Learn. 1(1), S1–S106 (1986).

    Google Scholar 

  • Gareth, J. et al. An Introduction to Statistical Learning: With Applications in R (Spinger, 2013).

    Google Scholar 

  • Zhang, H., The optimality of naïve Bayes. In FLAIRS2004 conference (2004).

  • Bland, J. M. & Altman, D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327(8476), 307–310 (1986).

    Article 

    Google Scholar 

  • Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Goodarzi, E. et al. Epidemiology of mortality induced by acute respiratory infections in infants and children under the age of 5 years and its relationship with the Human Development Index in Asia: An updated ecological study. J. Public Health 29, 1047–1054 (2021).

    Article 

    Google Scholar 

  • Harerimana, J.-M. et al. Social, economic and environmental risk factors for acute lower respiratory infections among children under five years of age in Rwanda. Arch. Public Health 74, 1–7 (2016).

    Article 

    Google Scholar 

  • Fenta, S. M. & Fenta, H. M. Risk factors of child mortality in Ethiopia: Application of multilevel two-part model. PloS one 15(8), e0237640 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chekroud, A. M. et al. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 20(2), 154–170 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kwon, J.-M. et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PloS one 14(7), e0219302 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Krittanawong, C. et al. Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection. Sci. Rep. 11(1), 8992 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bi, S. et al. Machine learning-based prediction of in-hospital mortality for post cardiovascular surgery patients admitting to intensive care unit: A retrospective observational cohort study based on a large multi-center critical care database. Comput. Methods Progr. Biome. 226, 107115 (2022).

    Article 

    Google Scholar 

  • Banda, W. et al. Risk factors associated with acute respiratory infections among under-five children admitted to Arthur’s Children Hospital, Ndola, Zambia. Asian Pac. J. Health Sci. 3(3), 153–159 (2016).

    Article 

    Google Scholar 

  • Vong, C.-M. et al. Short-term prediction of air pollution in Macau using support vector machines. J. Control Sci. Eng. 2012, 518032 (2012).

    Article 

    Google Scholar 

  • Cao, C., et al. Using support vector machine and decision tree to predict mortality related to traffic, air pollution, and meteorological exposure in Norway. In Three essays on Transportation and Environmental Economics, 70 (2023)

  • Schlink, U. et al. Longitudinal modelling of respiratory symptoms in children. Int. J. Biometeorol. 47, 35–48 (2002).

    Article 
    ADS 
    PubMed 

    Google Scholar 

  • Schwartz, J. Nonparametric smoothing in the analysis of air pollution and respiratory illness. Can. J. Stat. 22(4), 471–487 (1994).

    Article 

    Google Scholar 

  • Silva, D. R. et al. Respiratory viral infections and effects of meteorological parameters and air pollution in adults with respiratory symptoms admitted to the emergency room. Influenza Other Respir. Viruses 8(1), 42–52 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Tang, S. et al. Measuring the impact of air pollution on respiratory infection risk in China. Environ. Pollut. 232, 477–486 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Quinlan, J. R. Induction of decision trees. Mach. Learn. 1, 81–106 (1986).

    Article 

    Google Scholar 

  • Beam, A. L. & Kohane, I. S. Big data and machine learning in health care. Jama 319(13), 1317–1318 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Panch, T., Szolovits, P. & Atun, R. Artificial intelligence, machine learning and health systems. J. Global Health (2018).

    Article 

    Google Scholar 

  • Shahinfar, S. et al. Machine learning approaches for the prediction of lameness in dairy cows. Animal 15(11), 100391 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Omer, S. et al. Climatic, temporal, and geographic characteristics of respiratory syncytial virus disease in a tropical island population. Epidemiol. Infect. 136(10), 1319–1327 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jati, S. & Ginandjar, P. Potential impact of climate variability on respiratory diseases in infant and children in Semarang. In IOP Conference Series: Earth and Environmental Science (IOP Publishing, 2017).

  • Tian, L. et al. Spatial patterns and effects of air pollution and meteorological factors on hospitalization for chronic lung diseases in Beijing, China. Sci. China Life Sci. 62, 1381–1388 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Kanannejad, Z. et al. Geo-climatic variability and adult asthma hospitalization in Fars, Southwest Iran. Front. Environ. Sci. 11, 1085103 (2023).

    Article 

    Google Scholar 

  • Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol. 67(2), 301–320 (2005).

    Article 
    MathSciNet 

    Google Scholar 

  • Géron, A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (O’Reilly Media. Inc, 2022).

    Google Scholar 

  • Abdelhafiz, D. et al. Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinform. 20, 1–20 (2019).

    Article 

    Google Scholar 

  • Molina, M. & Garip, F. Machine learning for sociology. Ann. Rev. Sociol. 45, 27–45 (2019).

    Article 

    Google Scholar 

  • Aguilera, R. et al. Mediating role of fine particles abatement on pediatric respiratory health during COVID-19 stay-at-home order in San Diego County, California. GeoHealth 6(9), e2022GH000637 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Odo, D. B. et al. Ambient air pollution and acute respiratory infection in children aged under 5 years living in 35 developing countries. Environ. Int. 159, 107019 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Cai, Y. S. et al. Ambient air pollution and respiratory health in sub-Saharan African children: A cross-sectional analysis. Int. J. Environ. Res. Public Health 18(18), 9729 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fenta, H. M., Zewotir, T. & Muluneh, E. K. A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. BMC Med. Inform. Decis. Mak. 21(1), 1–12 (2021).

    Article 

    Google Scholar 

  • link