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A Real-Time Classification System of Thalassemic Pathologies Based on Artificial Neural Networks

Author

Listed:
  • S. R. Amendolia

    (Dipartimento di Matematica e Fisica, Università di Sassari, 07100 Sassari, Italy)

  • A. Brunetti

    (Dipartimento di Matematica e Fisica, Università di Sassari, 07100 Sassari, Italy)

  • P. Carta

    (Azienda Unità Sanitaria Locale n. 1 Distretto e Presidio Ospedaliero di Ozieri, Ozieri, Italy, Università di Sassari, 07100 Sassari, Italy)

  • G. Cossu

    (Azienda Unità Sanitaria Locale n. 1 Ospedale Civile di Sassari, 07100 Sassari, Italy, Università di Sassari, 07100 Sassari, Italy)

  • M. L. Ganadu

    (Dipartimento di Chimica, Università di Sassari, 07100 Sassari, Italy)

  • B. Golosio

    (Dipartimento di Matematica e Fisica, Università di Sassari, 07100 Sassari, Italy)

  • G. M. Mura

    (Dipartimento di Chimica, Università di Sassari, 07100 Sassari, Italy)

  • M. G. Pirastru

    (Azienda Unità Sanitaria Locale n. 1 Distretto e Presidio Ospedaliero di Ozieri, Ozieri, Italy, Università di Sassari, 07100 Sassari, Italy)

Abstract

Thalassemias are pathologies that derive from genetic defects of the globin genes. The most common defects among the population affect the genes that are involved in the synthesis of α and β chains. The main aspects of these pathologies are well explained from a biochemical and genetic point of view. The diagnosis is fundamentally based on hematologic and genetic tests. A genetic analysis is particularly important to determine the carriers of α -thalassemia, whose identification by means of the hematologic parameters is more difficult in comparison with heterozygotes for β -thalassemia. This work investigates the use of artificial neural networks (ANNs) for the classification of thalassemic pathologies using the hematologic parameters resulting from hemochromocytometric analysis only. Different combinations of ANNs are reported, which allow thalassemia carriers to be discriminated from normals with 94% classification accuracy, 92% sensitivity, and 95% specificity. On the basis of these results, an automated system that allows real-time support for diagnoses is proposed. The automated system interfaces a hemochromo analyzer to a simple PC.

Suggested Citation

  • S. R. Amendolia & A. Brunetti & P. Carta & G. Cossu & M. L. Ganadu & B. Golosio & G. M. Mura & M. G. Pirastru, 2002. "A Real-Time Classification System of Thalassemic Pathologies Based on Artificial Neural Networks," Medical Decision Making, , vol. 22(1), pages 18-26, February.
    • Handle: RePEc:sae:medema:v:22:y:2002:i:1:p:18-26
    DOI: 10.1177/0272989X0202200102
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    Cited by:

    1. Hsueh-Yi Lu & Chen-Yuan Huang & Chwen-Tzeng Su & Chen-Chiang Lin, 2014. "Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.

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