Health Scope

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Presenting a Model for Periodontal Disease Diagnosis Using Two Artificial Neural Network Algorithms

Samin Arbabi 1 , Farzad Firouzi Jahantigh 1 and Somayeh Ansari Moghadam 2 , *
Authors Information
1 Department of Industrial Engineering, School of Engineering, University of Sistan and Baluchestan, Zahedan, IR Iran
2 Oral and Dental Disease Research Center, Zahedan University of Medical Sciences, Zahedan, IR Iran
Article information

To Cite: Arbabi S, Firouzi Jahantigh F, Ansari Moghadam S. Presenting a Model for Periodontal Disease Diagnosis Using Two Artificial Neural Network Algorithms, Health Scope. Online ahead of Print ;In Press(In Press):e65330. doi: 10.5812/jhealthscope.65330.

Abstract
Copyright © 2018, Journal of Health Scope. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited
1. Background
2. Objectives
3. Methods
4. Results
5. Discussion
Acknowledgements
Footnote
References
  • 1. Ansari Moghaddam S, Abbasi S, Sanei Moghaddam E, Ansari Moghaddam A. Triglyceride and cholesterol levels in patients with chronic periodontitis. Health Scope. 2015;4(2). doi: 10.17795/jhealthscope-19928.
  • 2. Livingstone DJ. Artificial Neural Networks: Methods and Applications (Methods in Molecular Biology). Humana Press; 2008.
  • 3. Shankarapillai R, Mathur LK, Nair MA, George R. Periodontitis risk assessment using two artificial neural network algorithms–a comparative study. Int J Dent Clin. 2012;4(1).
  • 4. Milovic B. Prediction and decision making in health care using data mining. Kuwait Chap Arab J Bus Manag Rev. 2012;1(2). doi: 10.11591/ijphs.v1i2.1380.
  • 5. Sheikhpour R, Sarram MA. Diagnosis of diabetes using an intelligent approach based on bi-level dimensionality reduction and classification algorithms. Iran J Diabetes Obes. 2014;6(2):74-84.
  • 6. Ozden FO, Ozgonenel O, Ozden B, Aydogdu A. Diagnosis of periodontal diseases using different classification algorithms: a preliminary study. Niger J Clin Pract. 2015;18(3):416-21. doi: 10.4103/1119-3077.151785. [PubMed: 25772929].
  • 7. Kositbowornchai S, Plermkamon S, Tangkosol T. Performance of an artificial neural network for vertical root fracture detection: an ex vivo study. Dent Traumatol. 2013;29(2):151-5. doi: 10.1111/j.1600-9657.2012.01148.x. [PubMed: 22613067].
  • 8. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;106(6):879-84. doi: 10.1016/j.tripleo.2008.03.002. [PubMed: 18718785].
  • 9. Martina R, Teti R, D'Addona D, Iodice G. Neural network based system for decision making support in orthodontic extractions. Intelligent Production Machines and Systems. 2006. p. 235-40. doi: 10.1016/b978-008045157-2/50045-6.
  • 10. Amiri Z, Mohammad K, Mahmoudi M, Parsaeian M, Zeraati H. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models. Iran Red Crescent Med J. 2013;15(1):42-8. doi: 10.5812/ircmj.4122. [PubMed: 23486933]. [PubMed Central: PMC3589778].
  • 11. Shankarapillai R, Mathur LK, Nair MA, Rai N, Mathur A. Periodontitis risk assessment using two artificial neural networks-a pilot study. Int J Dent Clin. 2010;2(4).
  • 12. Moghimi S, Talebi M, Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2012;34(4):480-6. doi: 10.1093/ejo/cjr042. [PubMed: 21633091].
  • 13. Thohamtan RAM, Esmaeili MH, Ghaemian A, Esmaeili J. Application of artificial neural network for assessing coronary artery disease. J Mazandaran Univ Med Sci. 2012;22(86).
  • 14. Ainamo J, Ainamo A. Risk assessment of recurrence of disease during supportive periodontal care. Epidemiological considerations. J Clin Periodontol. 1996;23(3 Pt 2):232-9. [PubMed: 8707983].
  • 15. Page RC, Krall EA, Martin J, Mancl L, Garcia RI. Validity and accuracy of a risk calculator in predicting periodontal disease. J Am Dent Assoc. 2002;133(5):569-76. doi: 10.14219/jada.archive.2002.0232. [PubMed: 12036161].
  • 16. Zounemat Kermani M, Bay Y . [Efficiency analysis of artificial neural networks and multiple linear regression methods for tides prediction]. J Oceanogr. 2013;4(13):1-10. Persian.
  • 17. Menhaj M. Fundamentals of neural networks. Comput Intell. 1998;1(1).
  • 18. Honar T, Tarazkar M, Tarazkar M. Estimating of side weir discharge coefficient by using neuro-fuzzy (ANFIS). J Water Soil Conserv. 2010;17(2):169-76.
  • 19. Kuan CM, White H. Artificial neural networks: an econometric perspective∗. Econom Rev. 1994;13(1):1-91. doi: 10.1080/07474939408800273.
  • 20. Zhang G, Eddy Patuwo B, Y. Hu M. Forecasting with artificial neural networks. Int J Forecast. 1998;14(1):35-62. doi: 10.1016/s0169-2070(97)00044-7.
  • 21. Pappada SM, Cameron BD, Rosman PM. Development of a neural network for prediction of glucose concentration in type 1 diabetes patients. J Diabetes Sci Technol. 2008;2(5):792-801. doi: 10.1177/193229680800200507. [PubMed: 19885262]. [PubMed Central: PMC2769804].
  • 22. Safdari R, Ghazi Saeedi M, Zahmatkeshan M. [Information technology (IT): a new revolution in urban health development]. J Payavard Salamat. 2012;6(3):170-81. Persian.
  • 23. Ahmed FE. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer. 2005;4:29. doi: 10.1186/1476-4598-4-29. [PubMed: 16083507]. [PubMed Central: PMC1208946].
  • 24. Puddu PE, Menotti A. Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study. Eur J Cardiovasc Prev Rehabil. 2009;16(5):583-91. doi: 10.1097/HJR.0b013e32832d49e1. [PubMed: 19602982].
  • 25. Jafarnejad A, Soleymani M. [Demand forecasting medical equipment based on artificial neural networks and arima methods]. Q J Econ Res Polic. 2011;19(57):171-98. Persian.
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