Diagnosis of Headaches Types Using Artificial Neural Networks and Bayesian Networks

Authors

  • Amanda Trojan Fenerich PUCPR
  • Maria Teresinha Arns Steiner
  • Júlio Cesar Nievola
  • Karina Borges Mendes
  • Diego Paolo Tsutsumi
  • Bruno Samways dos Santos

Keywords:

Machine learning; artificial intelligence; headache diagnosis

Abstract

The aim of this paper is to classify several types of headaches from patients, using different data analysis methods, with the application of two classifying algorithms, comparatively: Bayesian Networks (BN) and Artificial Neural Networks (ANN). The data was collected through a data survey process applied to 2,177 patients with headache diagnosis in Neurological Clinic of municipality of Joinville/SC, Brazil, from January 2010 to November 2014. It was constructed eight different Test Models, varying: the attributes codification (codification A and B); the network output (one and five) and the classifier algorithms (BN and ANN). It is presented the accuracy results obtained from the eight Test Models, using the software WEKA® (Waikato Environment for Knowledge Analysis). The results presented a good accuracy in all tests realized and they suggest that BNs give better accuracy when comparing to ANNs for this problem.

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Published

2020-03-03

How to Cite

Fenerich, A. T., Steiner, M. T. A., Nievola, J. C., Mendes, K. B., Tsutsumi, D. P., & dos Santos, B. S. (2020). Diagnosis of Headaches Types Using Artificial Neural Networks and Bayesian Networks. IEEE Latin America Transactions, 18(1), 59–66. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/466

Issue

Vol. 18 No. 1 (2020): Ordinary Issue

Section

Articles

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