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An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches

Author

Listed:
  • Svetlana Simić

    (Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia)

  • José R. Villar

    (Faculty of Geology, Campus de Llamaquique, University of Oviedo, 33005 Oviedo, Spain)

  • José Luis Calvo-Rolle

    (Department of Industrial Engineering, University of A Coruña, 15405 Ferrol-A Coruña, Spain)

  • Slobodan R. Sekulić

    (Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Svetislav D. Simić

    (Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Dragan Simić

    (Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

Abstract

(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F 1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F 1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.

Suggested Citation

  • Svetlana Simić & José R. Villar & José Luis Calvo-Rolle & Slobodan R. Sekulić & Svetislav D. Simić & Dragan Simić, 2021. "An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches," IJERPH, MDPI, vol. 18(4), pages 1-14, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1890-:d:500054
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    References listed on IDEAS

    as
    1. Joshua K. Porter & Gian Luca Di Tanna & Richard B. Lipton & Sandhya Sapra & Guillermo Villa, 2019. "Costs of Acute Headache Medication Use and Productivity Losses Among Patients with Migraine: Insights from Three Randomized Controlled Trials," PharmacoEconomics - Open, Springer, vol. 3(3), pages 411-417, September.
    2. Svetlana Simić & Tamara Rabi-Žikić & José R. Villar & José Luis Calvo-Rolle & Dragan Simić & Svetislav D. Simić, 2020. "Impact of Individual Headache Types on the Work and Work Efficiency of Headache Sufferers," IJERPH, MDPI, vol. 17(18), pages 1-9, September.
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    1. Svetlana Simić & Tamara Rabi-Žikić & José R. Villar & José Luis Calvo-Rolle & Dragan Simić & Svetislav D. Simić, 2020. "Impact of Individual Headache Types on the Work and Work Efficiency of Headache Sufferers," IJERPH, MDPI, vol. 17(18), pages 1-9, September.

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