Multi-Agent Bayesian Framework For Parametric Selection In The Detection And Diagnosis of Tuberculosis Contagion In Nigeria
https://doi.org/10.35877/454RI.jinav375
Keywords:
tuberculosis, contagion, Bayesian Network, Nigeria, Deep Learning, epidemiology, reinforcement algorithm
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Abstract
Decision making has become quite a critical factor in our everyday living. The provision of data alongside its consequent processing has further sought to extend and expand our reasoning faculties as well as effectively aid proper decision making. But data is daily, produced at an exponential rapid rate and the volume in amount of data churned out to be processed even more so that we now require data storage optimization techniques to process such humongous volume of data. These have today, necessitated the need for advancement in data mining process. With the tremendous advances made in data mining, machine learning, storage virtualization and optimization – amongst other fields of computing – researchers now seek a new paradigm and platform called data science. This field today has become quite imperative as it seeks to provide beneficial support in constructing models and algorithms that can effectively assist domain experts and practitioners to make comprehensive and sound decisions regarding potential problematic cases. We focus on modeling social graph using implicit suggest algorithm in medical diagnosis to effectively respond to problematic cases of Tuberculosis (TB) in Nigeria. We introduce spectral clustering and Bayesian Network, construct algorithms cum models for predicting potential problematic cases in Tuberculosis as well as compare the algorithms based on data samples collected from an Epidemiology laboratory at the Federal Medical Center Asaba in Delta State of Nigeria. The volume of data was collated and divided into two data sets which are the training dataset and the investigation dataset. The model constructed by this study has shown a high predictive capability strength compared to other models presented on similar studies.
References
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Lin, J. H.., Haug, P.J., 2008. Exploiting missing clinical data in Bayesian network modeling for predicting medical problems, Journal of biomedical informatics, vol. 41, pp. 1-14
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Ojugo, A.A., Otakore, D.O., 2018. Improved early detection of gestational diabetes via intelligent classification models: a case of Niger Delta, J. of Comp. Sci. & Application, 6(2), pp. 82-90, doi: 10.12691/jcsa-6-2-5
Ojugo, A.A., Otakore, D.O., 2021. An intelligent Bayesian net to improve performance and dependability analysis of a campus network, Accepted in IAES Int. J. of Artificial Intelligence
Ojugo, A.A., Otakore, D., 2021. Forging optimized Bayesian network model with selected parameter for detection of Coronavirus in Delta State, J. of Appl. Sci., Engr., Tech. & Edu., 3(1): pp37–45, doi: 10.35877/454RI.asci2163
Ojugo, A.A., Otakore, D.O., Computational solution of networks versus cluster groupings for social network contacts: a recommender system, Int. J. of Info. & Comm. Tech., 9(3): pp13 – 27, 2020, doi: 10.11591/ijict.v9i3
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Ojugo, A.A., Otakore, O.D., 2020a. Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks, Int. J. Arti. Intel., 9(3): pp497~506, doi: 10.11591/ijai.v9.i3.pp497~506
Ojugo, A.A., Otakore, O.D., 2020b. Computational solution of networks versus cluster groupings for social network contacts recommender system, Int. J. Info. Comm. Tech., 9(3): pp185~194, doi: 10.11591/ijict.v9i3.pp185-194
Ojugo, A.A., Yoro, R.E., 2020. Empirical solution for an optimized machine learning framework for anomaly-based network intrusion detection, Tech. Report of Kansai Univ., TRKU-13-08-2020-10996, 62(10): pp6353-6364
Ojugo, A.A., Yoro, R.E., 2020. Empirical solution for an optimized machine learning framework for anomaly-network intrusion detection, Tech. Report of Kansai Univ., TRKU-13-08-2020-10996, 62(10): pp6353-6364
Ojugo, A.A., Yoro, R.E., 2021. Forging a deep learning neural network intrusion detection framework to curb distributed denial of service attack, Int. J. Elect. & Computer Engineering, Vol. 11, No. 2, pp 128-138
Parashar D, Kabra S, Lodha R, Singh V, Mukherjee A, Arya T, Grewal H, Singh S. 2013. Does neutralization of gastric aspirates from children with suspected intrathoracic tuberculosis affect mycobacterial yields on MGIT culture? J Clin Microbiol 51:1753–1756. http://dx.doi.org/10.1128/JCM.00202-13.
Pfyffer G. 2015. Mycobacterium: general characteristics, laboratory detection, and staining procedures, p 536–569. In Jorgensen J, Pfaller M, Carroll K, Funke G, Landry M, Richter S, WarnockD(ed), Manual of Clinical Microbiology, 11th ed. ASM Press, Washington, DC.
Sanchini A, Fiebig L, Drobniewski F, Haas W, Richter E, Katalinic- Jankovic V, et al., 2014. Laboratory diagnosis of paediatric tuberculosis in the European Union/European Economic Area: analysis of routine laboratory data, 2007-2011. Euro Surveil., 19(11):20744. [web]: www.eurosurveillance.org/ViewArticle.aspx?ArticleId_20744.
Singh, H et al., 2001. A Bayesian approach to reliability prediction and assessment of component based systems, pp.12
Stockdale A, Duke T, Graham S, Kelly J. 2010. Evidence behind the WHO guidelines: hospital care for children: what is the diagnostic accuracy of gastric aspiration for the diagnosis of tuberculosis in children? J Trop Pediatr 56:291–298. http://dx.doi.org/10.1093/tropej/fmq081.
Stolfo, S.J and Prodromidis, A.L, (1999). Agent-based distributed learning applied to fraud detection, Technical Report CUCS-014-99, Columbia University, 1999
Sun L, Xiao J, Miao Q, Feng W, Wu X, Yin Q, Jiao W, Shen C, Liu F, Shen D, Shen A. 2011. Interferon gamma release assay in diagnosis of pediatric tuberculosis: a meta-analysis. FEMS Immunol Med Microbiol 63:165–173. http://dx.doi.org/10.1111/j.1574-695X.2011.00838.x.
World Health Organization. Coronavirus Disease (2019) Situation Reports. [web: www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed on 2March 2020).
World Health Organization. Infectious Diseases Contagion Reports. Available online: www.who.int/emergencies/diseases/infectious-contagion-2015/situation-reports (March 2020).
World Health Organization. Tuberculosis contagion Situation Reports. Available online: https://www.who.int/emergencies/diseases/tuberculosis-2008/situation-reports (accessed on March 2020).
Zar H, Hanslo D, Apolles P, Swingler G, Hussey G. 2005. Induced sputum versus gastric lavage for microbiological confirmation of pulmonary tuberculosis in infants and young children: a prospective study. Lancet 365:130–134. http://dx.doi.org/10.1016/S0140-6736(05)17702-2.
American Academy of Pediatrics Committee on Infectious Diseases. 2015. Tuberculosis. In Kimberlin D, Brady MT, Jackson MA, Long S (ed), Red book: 2015 report of the Committee on Infectious Diseases, 30th ed. American Academy of Pediatrics, Elk Grove, IL.
Bouckaert, R.R. 2008. Bayesian Network Classifiers in Wekafor Version 3-5-7. Available: http://www.cs.waikato.ac.nz/~remco/weka_bn/index.html
Brittle W, Marais B, Hesseling A, Schaaf H, Kidd M, Wasserman E, Botha T. 2009. Improvement in mycobacterial yield and reduced time to detection in pediatric samples by use of a nutrient broth growth supplement. J Clin Microbiol 47:1287–1289. http://dx.doi.org/10.1128/JCM.02320-08.
Chiang S, Swanson D, Starke J. 2015. New diagnostics for childhood tuberculosis. Infect Dis Clin North Am 29:477–502. http://dx.doi.org/10.1016/j.idc.2015.05.011.
Cruz A, Starke J. 2014. Tuberculosis, p 1335–1380. In Cherry J, Harrison G, Kaplan S, Steinbach W, Hotez P (ed), Feigin and Cherry’s textbook of peidatric infectious diseases. Elsevier Saunders, Philadelphia, PA.
Friedman, N et al., 2000. Using Bayesian networks to analyze expression data, J. of Comp. Biology, 7, pp. 601-620
Kazmierska, J. and J. Malicki, 2008. Application of the Naive Bayesian Classifier to optimize treatment decisions," Radiother Oncol, vol. 86, pp. 211-216.
Lin, J. H.., Haug, P.J., 2008. Exploiting missing clinical data in Bayesian network modeling for predicting medical problems, Journal of biomedical informatics, vol. 41, pp. 1-14
Marais B, Gie R, Schaaf H, Hesseling A, Obihara C, Starke J, Enarson D, Donald P, Beyers N. 2004. The natural history of childhood intrathoracic tuberculosis: a critical review of literature from the prechemotherapy era. Int J Tuberc Lung Dis 8:392–402.
Mukherjee A, Singh S, Lodha R, Singh V, Hesseling A, Grewal H, Kabra S. 2013. Ambulatory gastric lavages provide better yields of Mycobacterium tuberculosis than induced sputum in children with intrathoracic tuberculosis. Pediatr Infect Dis J 32:1313–1317. http://dx.doi.org/10.1097/INF.0b013e31829f5c58.
Ojugo, A.A., Aghware, F., et al., 2015. Predicting behavioral evolution on a graph-based model, Advances in Networks, 3(2): pp8-21
Ojugo, A.A., Eboka, A.O., 2018. Comparative evaluation for high intelligent performance adaptive model for spam phishing detection, Digital Technology, Vol. 3, No.1: pp. 9-15, doi: 10.1269/dt-3-1-1, 2018
Ojugo, A.A., Eboka, A.O., 2020. Empirical evaluation on comparative study of machine learning techniques in detection of DDoS, J. of Applied Sci., Engr., Tech. & Edu., 2(1), pp18–27, doi: 10.35877/454RI.asci2192
Ojugo, A.A., Eboka, A.O., 2021. Modeling behavioral evolution as social predictor for coronavirus contagion and immunization in Nigeria, J. of Applied Sci., Engr., Tech. & Educ., 3(2): pp37–45, doi: 10.35877/454RI.asci130
Ojugo, A.A., Okobah, I.P., 2018. Prevalence rate of hepatitis-B virus infection in Niger Delta region of Nigeria using graph-based diffusion heuristic model, IJCAOnline Int. J. Computer Application, 179(39): pp27–33
Ojugo, A.A., Otakore, D.O., 2018. Improved early detection of gestational diabetes via intelligent classification models: a case of Niger Delta, J. of Comp. Sci. & Application, 6(2), pp. 82-90, doi: 10.12691/jcsa-6-2-5
Ojugo, A.A., Otakore, D.O., 2021. An intelligent Bayesian net to improve performance and dependability analysis of a campus network, Accepted in IAES Int. J. of Artificial Intelligence
Ojugo, A.A., Otakore, D., 2021. Forging optimized Bayesian network model with selected parameter for detection of Coronavirus in Delta State, J. of Appl. Sci., Engr., Tech. & Edu., 3(1): pp37–45, doi: 10.35877/454RI.asci2163
Ojugo, A.A., Otakore, D.O., Computational solution of networks versus cluster groupings for social network contacts: a recommender system, Int. J. of Info. & Comm. Tech., 9(3): pp13 – 27, 2020, doi: 10.11591/ijict.v9i3
Ojugo, A.A., Otakore, D.O., Intelligent cluster connectionist recommender system using Implicit graph friendship algorithm for social networks, Int. J. Artificial Intel., 9(3): pp429-439, 2020, doi: 10.11591/ijai.v9.i3.pp429-439
Ojugo, A.A., Otakore, O.D., 2020a. Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks, Int. J. Arti. Intel., 9(3): pp497~506, doi: 10.11591/ijai.v9.i3.pp497~506
Ojugo, A.A., Otakore, O.D., 2020b. Computational solution of networks versus cluster groupings for social network contacts recommender system, Int. J. Info. Comm. Tech., 9(3): pp185~194, doi: 10.11591/ijict.v9i3.pp185-194
Ojugo, A.A., Yoro, R.E., 2020. Empirical solution for an optimized machine learning framework for anomaly-based network intrusion detection, Tech. Report of Kansai Univ., TRKU-13-08-2020-10996, 62(10): pp6353-6364
Ojugo, A.A., Yoro, R.E., 2020. Empirical solution for an optimized machine learning framework for anomaly-network intrusion detection, Tech. Report of Kansai Univ., TRKU-13-08-2020-10996, 62(10): pp6353-6364
Ojugo, A.A., Yoro, R.E., 2021. Forging a deep learning neural network intrusion detection framework to curb distributed denial of service attack, Int. J. Elect. & Computer Engineering, Vol. 11, No. 2, pp 128-138
Parashar D, Kabra S, Lodha R, Singh V, Mukherjee A, Arya T, Grewal H, Singh S. 2013. Does neutralization of gastric aspirates from children with suspected intrathoracic tuberculosis affect mycobacterial yields on MGIT culture? J Clin Microbiol 51:1753–1756. http://dx.doi.org/10.1128/JCM.00202-13.
Pfyffer G. 2015. Mycobacterium: general characteristics, laboratory detection, and staining procedures, p 536–569. In Jorgensen J, Pfaller M, Carroll K, Funke G, Landry M, Richter S, WarnockD(ed), Manual of Clinical Microbiology, 11th ed. ASM Press, Washington, DC.
Sanchini A, Fiebig L, Drobniewski F, Haas W, Richter E, Katalinic- Jankovic V, et al., 2014. Laboratory diagnosis of paediatric tuberculosis in the European Union/European Economic Area: analysis of routine laboratory data, 2007-2011. Euro Surveil., 19(11):20744. [web]: www.eurosurveillance.org/ViewArticle.aspx?ArticleId_20744.
Singh, H et al., 2001. A Bayesian approach to reliability prediction and assessment of component based systems, pp.12
Stockdale A, Duke T, Graham S, Kelly J. 2010. Evidence behind the WHO guidelines: hospital care for children: what is the diagnostic accuracy of gastric aspiration for the diagnosis of tuberculosis in children? J Trop Pediatr 56:291–298. http://dx.doi.org/10.1093/tropej/fmq081.
Stolfo, S.J and Prodromidis, A.L, (1999). Agent-based distributed learning applied to fraud detection, Technical Report CUCS-014-99, Columbia University, 1999
Sun L, Xiao J, Miao Q, Feng W, Wu X, Yin Q, Jiao W, Shen C, Liu F, Shen D, Shen A. 2011. Interferon gamma release assay in diagnosis of pediatric tuberculosis: a meta-analysis. FEMS Immunol Med Microbiol 63:165–173. http://dx.doi.org/10.1111/j.1574-695X.2011.00838.x.
World Health Organization. Coronavirus Disease (2019) Situation Reports. [web: www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed on 2March 2020).
World Health Organization. Infectious Diseases Contagion Reports. Available online: www.who.int/emergencies/diseases/infectious-contagion-2015/situation-reports (March 2020).
World Health Organization. Tuberculosis contagion Situation Reports. Available online: https://www.who.int/emergencies/diseases/tuberculosis-2008/situation-reports (accessed on March 2020).
Zar H, Hanslo D, Apolles P, Swingler G, Hussey G. 2005. Induced sputum versus gastric lavage for microbiological confirmation of pulmonary tuberculosis in infants and young children: a prospective study. Lancet 365:130–134. http://dx.doi.org/10.1016/S0140-6736(05)17702-2.
Published
2021-03-05
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How to Cite
Ojugo, A. A., & Nwankwo, O. (2021). Multi-Agent Bayesian Framework For Parametric Selection In The Detection And Diagnosis of Tuberculosis Contagion In Nigeria. JINAV: Journal of Information and Visualization, 2(2), 69-76. https://doi.org/10.35877/454RI.jinav375
Copyright (c) 2021 Arnold Adimabua Ojugo, Obinna Nwankwo
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