Development of a Model for Recurrent Tonsillitis in Paediatric Patient
Clinical Medicine Research
Volume 8, Issue 5, September 2019, Pages: 101-114
Received: Jul. 14, 2019; Accepted: Aug. 5, 2019; Published: Oct. 12, 2019
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Omolola Abike Akintola, Department of Computer Science & Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Samuel Ademola Adegoke, Department of Pediatrics and Child Health, Obafemi Awolowo University, Ile-Ife, Nigeria
Adanze Onyenonachi Asinobi, Department of Pediatrics, College of Medicine, University of Ibadan, Ibadan, Nigeria
Temilade Aderounmu, Department of Pediatrics and Child Health Care, Obafemi Awolowo University Teaching Hospital Complex, Ile-Ife, Nigeria
Victor Oluwatimilehin Adebayo, Department of Computer Science & Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Peter Adebayo Idowu, Department of Computer Science & Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
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This study identifies the risk factors of recurrent tonsillitis in pediatric patient which in turn are the variables used in developing a predictive model for predicting the risk of recurrent tonsillitis. This is achieved by eliciting knowledge on the risk factors of recurrent tonsillitis, formulating the model using the variables and simulating the model using MATLAB tool. Interviews were conducted with the pediatrician and existing literature was studied on the knowledge of study in order to identify the variables for recurrent tonsillitis. Seven (7) data from tonsillitis patients were collected from Wesley Guild Hospital, Ilesha. Predictive model was formulated using the fuzzy logic model and simulated on MATLAB R2016a. Fuzzy logic was used as the predictive model to determine the risk of recurrent tonsillitis. The stages involved in the process are four (4) which includes: fuzzification, rule production, aggregation and defuzzification. The identified variables were given crisp values and within a membership function of 0 and 1. The simulated result of the fuzzy logic model was done using MATLAB which involved formulation of the fuzzy logic inference system (FIS) which was carried out by the MATLAB tool. The variables which are the risk factors were used to build the fuzzy logic inference system (FIS) to determine the risk of recurrent tonsillitis. Possible combinations of rules were given for the variables and the rules were used in the inference engine to predict the output of the model whether it is no, low, moderate or high risk of recurrent tonsillitis. The validation was done on the data gotten from Wesley Guild Hospital Ilesha from 7 patients. In conclusion, out of the seven (7) patients test data provided, five (5) patients have low risk, two (2) patients have moderate risk, no patients have no low risk and no patients have high risk of recurrent tonsillitis with 100% test accuracy.
Recurrent Tonsillitis, Model, Pediatric Patient, Fuzzy Logic, Inference System
To cite this article
Omolola Abike Akintola, Samuel Ademola Adegoke, Adanze Onyenonachi Asinobi, Temilade Aderounmu, Victor Oluwatimilehin Adebayo, Peter Adebayo Idowu, Development of a Model for Recurrent Tonsillitis in Paediatric Patient, Clinical Medicine Research. Vol. 8, No. 5, 2019, pp. 101-114. doi: 10.11648/j.cmr.20190805.13
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Spinks, A., Glasziou, P. P., and Del Mar, C. B. (2013). Antibiotics for Sore Throat. Cochrane Database of Systematic Reviews, 11. Van-den-Anker, J. N. (2013). Optimising Management of Fever and Pain in Children. International Journal of Clinical practice, 67 (s178), 26-32.
Van-Staaij, B., Van-den-Akker, E., Rovers, M., Hordijk, G., Hoes, A., and Schilder, A. (2004). Adenotonsillectomy in Children: a Randomised Trial. British Medical Journal. 40 (2), 435-439.
Nuha, S., O. M., R., Rakan, M., Majed, A., Rawan, M., and Saleem, O. (2017). Causes and Treatment of Tonsillitis. The Egyptian Journal of Hospital Medicine, 69 (8): 2975-2980.
Pradeep, K. S. (2012). Acute Tonsillitis - If Left Untreated Could Cause Several Fatal Complications. Journal of Current Clinical Care, 2: 29-34.
Brandtzaeg, P. (2011). Immune Functions of Nasopharyngeal Lymphoid Tissue. In Recent Advances in Tonsils and Mucosal Barriers of the Upper Airways, 72 (3): 20-24.
Morad, A., sathe, N. A., Francis, D. O., McPheeters, M. L., and Chinnadurai, S. (2017). Tonsillectomy Versus Watchful Waiting for Recurrent Throat Infection: A Systematic Review. Paediatrics, 139 (2): 1-12.
Altamimi, S., Khalil, A., Khalaiwi, K., Milner, R., Pusic, M., and Al Othman, M. (2012). Short-term Late-generation Antibiotics Versus Longer Term Penicillin for Acute Streptococcal Pharyngitis in Children. The Cochrane library, Rev 8: CD004872. Retrieved from [Accessed 12 July, 2018) ]
Ali, M., and Al-Shehri, M. (2014). Incidence and Potential Risk Factors of Post-Tonsillectomy Hemorrhage. Bahrain Medical Bulletin, 36 (3): 204
Chiappini, E., Regoli, M., Bonsignori, F., Sollai, S., Parretti, A., and Galli, L. (2011). Analysis of Different Recommendations from International Guidelines for the Management of Acute Pharyngitis in Adults and Children. Clinical Therapeutics, 33 (1): 48-58.
Spektor, Z. S. -V., Kay, D., and Mandell, D. (2016). Risk Factors for Pediatric Post-tonsillectomy Hemorrhage. International Journal of Pediatric Otorhinolaryngology, 84 (13): 151-155.
Vijayashree, M. S., Viswanatha, B., and Sambamurthy, B. N. (2014). Clinical and Bacteriological Study of Acute Tonsillitis. IOSR Journal of Dental and Medical Sciences, 13 (1): 37-43.
Reid, D., Mortan, R., Salkein, L., and Bartely, J. (2011). Vitamin D and Tonsil Disease: Preliminary Observations. International Journal of Paediatric Otorhinolaryngology, 75 (2): 2614.
Sarah, Y., Sabry, A., and Esteglal, E. (2014). Isolation and Identification of Microorganisms Causing Tonsillitis among Children of Hail Region. International Journal of Health Sciences and Reseacrh, 100 (4): 125-129.
Zhang, P., Schmidt, D. C., White, J., and Lenz, G. (2018). Chapter One - Blockchain Technology Use Cases in healthcare. Advances in Computer, 111: 1-41.
Rampratap, T. (2016). Modeling for Fault Tolerance in Cloud Computing Environment. Journal of Computer Sciences and Applications, 40 (4): 9-13.
Desikan, P. and Khare, R. (2013). Data Mining for Healthcare: Workshop Summary. International Conference on Health Informatics. Philadelphia, PA.
Butler, C., Hood, K., Kinnersley, P., Robling, M., Prout, H., and Houston, H. (2005). Predicting the Clinical Course of Suspected Acute Viral Upper Respiratory Tract Infection in Children. Family Practice, 22 (1):, 92-95.
Moore, M., Stuart, B., Little, P, Smith, S., Thompson, M. J., Knox, K., van den Bruel, A., Lown, M. and Mant, D. (2017). Predictors of Pneumona in Lower Respiratory Tract Infections: 3C Prospective Cough Complication Cohort Study. European Respiratory Journal, 50: 1-9.
Cohen, J. F., Cohen, R., Bidet, P., Elbez, A., Levy, C., Bossuyt, P. M and Chalumeau, M. (2017). Efficiency of a Clinical Prediction Model for Selective Rapid Testing in Children with Pharyngitis: A Perspective, Multicenter Study. PLOS One, 12 (2): 1-11.
Aalbers, J., O'brien, K. K., Chan, W., Falk, G. A., Teljeur, C., Dimitrov, D., and Fahey, T. (2011). Predicting Streptococcal Pharyngitis in Adults in Primary Care: A SystematicReview of the Diagnostic Accuracy of Symptoms and Signs and Validation of the Center Score. BMC Medicine, 9 (67): 1-11.
Attia, M. (1999). Multivariate Predictive Models for Group A Beta-hemolytic Streptococcal Pharyngitis in Children. Academy Emergency Medicine, 20 (2): 813-820.
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