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Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients

Author

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  • Reza Safdari

    (Department of Health Information Technology, Tehran University of Medical Sciences, Tehran, Iran)

  • Peyman Rezaei-Hachesu

    (Department of Health Information Technology, Tabriz University of Medical Sciences, Tabriz, Iran)

  • Marjan GhaziSaeedi

    (Department of Health Information Technology, Tehran University of Medical Sciences, Tehran, Iran)

  • Taha Samad-Soltani

    (Department of Health Information Technology, Tabriz University of Medical Sciences, Tabriz, Iran)

  • Maryam Zolnoori

    (National Library of Medicine, Bethesda, USA)

Abstract

Medical data mining intends to solve real-world problems in the diagnosis and treatment of diseases. This process applies various techniques and algorithms which have different levels of accuracy and precision. The purpose of this article is to apply data mining techniques to the diagnosis of asthma. Sensitivity, specificity and accuracy of K-nearest neighbor, Support Vector Machine, naive Bayes, Artificial Neural Network, classification tree, CN2 algorithms, and related similar studies were evaluated. ROC curves were plotted to show the performance of the authors' approach. Support vector machine (SVM) algorithms achieved the highest accuracy at 98.59% with a sensitivity of 98.59% and a specificity of 98.61% for class 1. Other algorithms had a range of accuracy greater than 87%. The results show that the authors can accurately diagnose asthma approximately 98% of the time based on demographics and clinical data. The study also has a higher sensitivity when compared to expert and knowledge-based systems.

Suggested Citation

  • Reza Safdari & Peyman Rezaei-Hachesu & Marjan GhaziSaeedi & Taha Samad-Soltani & Maryam Zolnoori, 2018. "Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 10(2), pages 22-35, April.
    • Handle: RePEc:igg:jisss0:v:10:y:2018:i:2:p:22-35
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