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Anemia Diagnosis By Using Artificial Neural Networks

Year 2020, Volume: 4 Issue: 1, 14 - 17, 31.07.2020

Abstract

In the last years, applications of artificial intelligence for diagnosis of diseases as a decision support system has been widely used. In these applications, the state that if the patient contracts to the suspected is classified as positive or negative. Although in the previous works which use artificial neural networks various diseases especially some cancer types have been studied, anemia has remained as an disease that has not been focused. Anemia which can emerge due to the degeneration of blood structure, blood loss or elimination of erythrocytes is a disease that is widely encountered and can result in significant health problems. In this study, a decision support system using artificial neural network has been proposed for the diagnosis of anemia according to the selected comprehensive blood laboratory test.

References

  • [1] M. Behnam, A. Mohammadhossein, C. Shing, “A medical decision support system for disease diagnosis under uncertainty”, Expert Systems With Applications 88, 2017.
  • [2] J. Jingchi, L. Xueli, Z. Chao, G. Yi, Y. Qiubin, “Learning and inference in knowledge-based probabilistic model for medical diagnosis”, Knowledge-Based Systems, 2017.
  • [3] J. Amin, M. Mohammad, “Fuzzy Evidential Network and Its Application as Medical Prognosis and Diagnosis Models”, Journal of Biomedical Informatics, 2017.
  • [4] S. Gandhi, C. Edgar, L. Jose, E. Marisol, R. Alejandro, P. Yuliana, “Collective intelligence in medical diagnosis systems: A case study”, Computers in Biology and Medicine, 2016.
  • [5] R. Alejandro, A. Giner, “An approach for solving multi-level diagnosis in high sensitivity medical diagnosis systems through the application of semantic Technologies”, Computers in Biology and Medicine 43, 2013. [6] E. McLean, M. Cogswell, I. Egli, D. Wojdyla, B. Benoist, “Worldwide prevalence ofanaemia, who vitamin and mineral nutrition information system”, Public Health Nutr.”, 2009.
  • [7] I. Anand, J. McMurray, J. Whitmore, M. Warren, A. Pham, A. McCamish, P. Burton, “Anemia and its relationship to clinical outcome in heart failure”, Circulation, 2004.
  • [8] H. Erdem, A. Berkol, M. Sert, “Comparative Study of Universal Function Approximators (Neural Network, Fuzzy Logic, ANFIS) for Non-Linear Systems” International Journal of Scientific Research in Information Systems and Engineering (IJSRISE) 2015.
  • [9] O. Unal, A. Berkol, E. Tartan, “Using Artificial Intelligence Based Expert System for Selection of Design Subcontractors: A Case Study in Aerospace Industry”, 8th IEEE International Conference on Mechanical and Aerospace Engineering (ICMAE 2017).
  • [10] T. Subashini, V. Ramalingam, S. Palanivel, “Breast mass classification based on cytological patterns using RBFNN and SVM”, Expert Syst. Appl., vol. 36, no. 3, pp. 5284–5290, Apr. 2009.
  • [11] B. Djavan, M. Remzi, A. Zlotta, C. Seitz, P. Snow, M. Marberger “Artificial Neural Network for Early Detection of Prostate Cancer”. J-Clin Oncol 2002; 20:921–9.
Year 2020, Volume: 4 Issue: 1, 14 - 17, 31.07.2020

Abstract

Supporting Institution

Yücelen Grup

Thanks

Dr. Ali Yücelen

References

  • [1] M. Behnam, A. Mohammadhossein, C. Shing, “A medical decision support system for disease diagnosis under uncertainty”, Expert Systems With Applications 88, 2017.
  • [2] J. Jingchi, L. Xueli, Z. Chao, G. Yi, Y. Qiubin, “Learning and inference in knowledge-based probabilistic model for medical diagnosis”, Knowledge-Based Systems, 2017.
  • [3] J. Amin, M. Mohammad, “Fuzzy Evidential Network and Its Application as Medical Prognosis and Diagnosis Models”, Journal of Biomedical Informatics, 2017.
  • [4] S. Gandhi, C. Edgar, L. Jose, E. Marisol, R. Alejandro, P. Yuliana, “Collective intelligence in medical diagnosis systems: A case study”, Computers in Biology and Medicine, 2016.
  • [5] R. Alejandro, A. Giner, “An approach for solving multi-level diagnosis in high sensitivity medical diagnosis systems through the application of semantic Technologies”, Computers in Biology and Medicine 43, 2013. [6] E. McLean, M. Cogswell, I. Egli, D. Wojdyla, B. Benoist, “Worldwide prevalence ofanaemia, who vitamin and mineral nutrition information system”, Public Health Nutr.”, 2009.
  • [7] I. Anand, J. McMurray, J. Whitmore, M. Warren, A. Pham, A. McCamish, P. Burton, “Anemia and its relationship to clinical outcome in heart failure”, Circulation, 2004.
  • [8] H. Erdem, A. Berkol, M. Sert, “Comparative Study of Universal Function Approximators (Neural Network, Fuzzy Logic, ANFIS) for Non-Linear Systems” International Journal of Scientific Research in Information Systems and Engineering (IJSRISE) 2015.
  • [9] O. Unal, A. Berkol, E. Tartan, “Using Artificial Intelligence Based Expert System for Selection of Design Subcontractors: A Case Study in Aerospace Industry”, 8th IEEE International Conference on Mechanical and Aerospace Engineering (ICMAE 2017).
  • [10] T. Subashini, V. Ramalingam, S. Palanivel, “Breast mass classification based on cytological patterns using RBFNN and SVM”, Expert Syst. Appl., vol. 36, no. 3, pp. 5284–5290, Apr. 2009.
  • [11] B. Djavan, M. Remzi, A. Zlotta, C. Seitz, P. Snow, M. Marberger “Artificial Neural Network for Early Detection of Prostate Cancer”. J-Clin Oncol 2002; 20:921–9.
There are 10 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ali Berkol

Emre Tartan

Yahya Ekici This is me

Publication Date July 31, 2020
Submission Date January 28, 2020
Published in Issue Year 2020 Volume: 4 Issue: 1

Cite

IEEE A. Berkol, E. Tartan, and Y. Ekici, “Anemia Diagnosis By Using Artificial Neural Networks”, IJMSIT, vol. 4, no. 1, pp. 14–17, 2020.